I moderate the Reddit community r/UFOs, one of the largest online UFO communities It produces a constant stream of sighting reports. That stream is the best and worst thing about the subreddit. On a good night someone uploads clear footage of something that resists explanation, three other people in the same county chime in, and you remember why you volunteered for this. On a normal night it is a blurry dot filmed through a window screen, titled “WHAT IS THIS ORB??”, posted by someone who has never heard of Starlink.

A few months ago we ran a thread asking the community how to improve the sub. One comment stuck with me:

A lot of the sighting posts here are easily explainable as balloons, clouds, out-of-focus drones and aircraft, or Venus/Jupiter. It does get old after a while. I don't know what the solution is, maybe require sighting posts to include why they're not those things somehow.

That comment became a spec. The result is ufosighting.report, a permanent, searchable, media-backed archive of the community's sightings, plus a submission pipeline designed to raise the floor on report quality. This post is the longer story behind it: what it does, what the data revealed once I could see it in aggregate, and some notes on how it is built.

The quiet problem: sightings rot

Before the quality problem, there is a preservation problem that most people never notice.

Reddit is a terrible archive. Users delete their accounts and take their posts with them. Posts get removed. And even threads that survive slowly decay, because Reddit re-encodes uploaded media aggressively and old media links eventually stop resolving. Some of the most interesting sighting reports from even two years ago are now text skeletons with dead video embeds. If any of this material ever turns out to matter, it will matter as a body of evidence, and bodies of evidence should not depend on whether a stranger keeps their Reddit account.

So the first job of the site is boring and important: every post flaired “Sighting” on r/UFOs is ingested automatically, media and all. The video files, the images, the top comments, the original text. Around 8,000 sightings going back to early 2024 are already in, and new ones arrive within minutes of being posted. If the author later deletes their post, the archive keeps it, clearly labeled with what happened to the original. Reports that our own mod team removed for breaking the rules stay stored but out of the public archive; spam does not become history just by getting old.

Turning prose into pins

A Reddit sighting post is unstructured prose. “Saw this over the lake near my house around 9 last night” is a location and a time to a human and neither to a database.

The pipeline runs every incoming post (title, body, and the author's follow-up comments, because the useful details are always in a comment) through an LLM extraction step that pulls out the date, time, timezone, and location when they are actually stated, then geocodes the location to coordinates. I deliberately do not let it guess object shape, size, or behavior; free text is too easy to over-read, and hallucinated structure is worse than none. Dates and places it cannot corroborate stay blank.

About four in five archived sightings ended up with usable coordinates. That is what makes the map possible: nearly seven thousand pins, filterable by date. Set the date range to mid-November through late December 2024 and watch the New Jersey drone flap bloom across the East Coast. It is one thing to remember that period as a news cycle and another to see it as a point cloud.

What the data says once you can actually query it

Here is where it got fun. Once thousands of reports are structured, you can ask questions that are impossible to ask of a subreddit.

The hotspot map you expect is a population map. Every raw heatmap of UFO sightings lights up over big cities, because that is where the people are. Texas ranks second in raw sighting counts. Adjust for population and Texas falls to 35th. California drops from first to 15th. The map has a per-capita mode that normalizes each area against its local population, and when you flip it on, the glow migrates away from the metros and settles over the desert Southwest: Arizona, Nevada, New Mexico, plus a hot stripe of coastal New Jersey from the drone flap. Phoenix Lights country, Area 51 country, Roswell country. I make no claims about why. Reporting culture, dark skies, military airspace, and folklore all plausibly feed it. But the pattern is real and it is not where the people are.

Military bases are nearby more often than chance suggests. In the highest-anomaly states, 56 percent of sightings fall within 50 kilometers of a military installation, against 43 percent elsewhere. Bases sit on exactly the kind of open land that produces good sky views, so treat that gap gently. It is still an interesting gap.

A lot of UFOs have prosaic flight plans. For every mapped sighting with a time and place, the site computes what was actually overhead using orbital data: ISS passes, Starlink trains, bright satellites, and rocket launches. Of the sightings checked so far, roughly one in five coincided with a Starlink train visible from that location at that time. Two hundred and eighteen lined up with an ISS pass. Sixty-two happened within hours and a few hundred kilometers of a rocket launch, and a twilight rocket plume is responsible for more panicked video than almost anything else in the sky. Each sighting page shows this context automatically, along with links to historical aircraft traffic for that day and a sky chart for that exact spot and hour. The point is not to debunk anyone. The point is that “what else was up there” should be one click, not one research project.

Raising the floor on new reports

The other half of the site is the submission wizard, which is my answer to that comment from the feedback thread.

Posting a sighting through it requires actually describing the event: where, when, shape, movement, duration, distance, the “five observables” questions, and, centrally, a mandatory field asking why this is not a common object. You have to write down what you ruled out before the community sees it. It is friction, on purpose. Low-effort reports are cheap to produce and expensive for everyone else to wade through, and a little typing rebalances that.

Then there is the media handling, which I think is the strongest technical argument for the site:

• Originals are preserved untouched. Reddit re-encodes everything you upload. The site keeps the exact file, byte for byte. Upload from an iPhone and the original HEIC goes into storage, not a recompressed JPEG.
• Metadata becomes evidence, with consent. On upload, the site reads the file's EXIF: camera model, lens, exposure, capture time, GPS, even the compass heading you were facing. For a sighting, that heading is gold; it lets anyone reconstruct your exact view of the sky and check it against satellite and aircraft positions. You see everything the file contains before publishing, with checkboxes to withhold device details, timestamp, or location.
• Withheld means scrubbed, not hidden. If you choose not to share GPS, the coordinates are stripped from the published file itself. Privacy that depends on nobody checking is not privacy.

Finally, submissions are tied to Reddit identity. After you submit, a bot DMs you a verification link, and only after you confirm does the sighting go live and post to r/UFOs under your username, marked verified. Anonymous drive-by noise gets expensive; standing behind your report gets easy.

What is next

The shape and object data on new submissions will compound over time into something genuinely queryable, and I would like the community to help enrich the older records the same way a wiki fills in gaps. More viewing angles on the data are coming. And if you have ideas, the whole point of building this for a community is that the community gets a say.

Browse the archive at ufosighting.report, explore the map, and if you see something in the sky you cannot explain, put it on the record. Properly, this time. With the metadata.

Texas Rangers vs Atlanta Braves

My MLB game of choice this Saturday has my Texas Rangers playing the Atlanta Braves again in a game that started at 3:15 PM CDT. I joined the game in progress with the Rangers leading 1 to 0 in the first inning. As I usually do, I'm follow the game's score and stats in real time via MLB's Gameday Service where we can also find links to the radio-call of the game provided by announcers of either team we choose.

And the adventure continues.

i have absolutely 25 minutes to write about whatever happend today, and today was so much of a mess that i slept half of it and forgot alot of stuff. including this. I visited my mom today. people usually bring flowers to graves. i brought paranoia. Something pushed me. at least i think it did. then the worms again. they werent suppose to be there. i know they werent. so either my mother has developed a sense of humor, or my brain is still trying to kill me in the least creative way possible. i ran. i didnt want to. but i had that nightmare again. and once you’ve watched worms crawl out of your skin enough times, you stop asking questions and just start running. im angry about it. does that change anything? No. i also got pink flowers today. i hate flowers. i hate pink even more. i dont know who sent them. It wasn’t here im sure of that. i thought about asking her anyway, but i’d rather keep one thing to myself before i accidentally make another conversation weird. im still not sleeping properly. showering has become an argument with my own reflection. every drain looks guilty. every shadow feels alive and thats just. thats exhuasting. I keep thinking about my mother. even she had enough of me. i dont know what i expected from visiting a grave, but being pushed toward worms wasn’t on my list. everyone says your mother is supposed to be the person who protects you, and thats funny now that i think about it, because mine apparently decided i needed a closer look at the ground. maybe she dislikes me and shes just tired of me. either way, im not exactly rushing to ask for a second visit. or maybe she’s disappointed about something i’ve done, but still.

Anyway, my twenty five minutes are almost gone, and im not skipping today. Congrats, you got whatever this was. a rush entry, a confused brain and a man losing arguments with things that may or may not exist. enjoy.

Sincerely, Mother’s mistake

Celebrating 100 years of Moody Radio, connecting generations through timeless broadcasts and iconic moments captured in history.

This is a year of anniversaries. Moody Bible Institute turns 140 and Moody Radio turns 100. I listen to Moody Radio almost daily. I think Moody Church and Moody Bible Institute are the premier Evangelical Christian sword in today's world. You can find a great timeline with historical pictures here. Happy 140 years, Moody Bible Institute, keep turning out those Christian men and women. Happy 100 years to Moody Radio. Here's to another 100 years, unless the Lord tarries.

---

Links may be shortened via mtribe.link for cleaner formatting. All links redirect to their original destinations.

#Christianity #history #inspiration

چگونه انتخاب یک اتاق یخچالی مناسب هزینه‌های حمل‌ونقل را کاهش داد؟

یکی از فعالان حوزه توزیع مواد غذایی که سال‌ها با مشکلات حمل محصولات حساس به دما مواجه بود، تصمیم گرفت ناوگان خود را به اتاق‌های یخچالی استاندارد مجهز کند.

پیش از این، نوسانات دمایی باعث کاهش کیفیت برخی محصولات و افزایش هزینه‌های عملیاتی شده بود. بررسی‌های انجام شده نشان داد بخش زیادی از این مشکلات به کیفیت پایین عایق‌بندی و ساختار نامناسب اتاق بار مربوط می‌شود.

پس از انتخاب یک اتاق یخچالی با عایق مناسب و طراحی استاندارد، شرایط حمل‌ونقل به شکل محسوسی تغییر کرد. دمای داخل اتاق در طول مسیر پایدارتر شد و میزان ضایعات کاهش یافت.

کارشناسان معتقدند هنگام خرید اتاق یخچالی باید به عواملی مانند ضخامت عایق، جنس بدنه، سیستم سرمایشی و خدمات پس از فروش توجه ویژه داشت.

شاهان دژ به عنوان یکی از مجموعه‌های فعال در زمینه تولید اتاق یخچالی، انواع اتاق‌های یخچالی را برای خودروهای سبک و سنگین تولید می‌کند و راهکارهای متنوعی برای نیازهای مختلف حمل‌ونقل ارائه می‌دهد.

تجربه بسیاری از فعالان این حوزه نشان می‌دهد که انتخاب صحیح اتاق یخچالی نه‌تنها کیفیت حمل کالا را افزایش می‌دهد، بلکه در بلندمدت باعث کاهش هزینه‌های نگهداری و افزایش بهره‌وری نیز می‌شود.

در سال‌های اخیر رشد صنعت مواد غذایی، دارویی و فروشگاه‌های زنجیره‌ای باعث افزایش چشمگیر نیاز به ناوگان حمل‌ونقل سردخانه‌ای شده است. کارشناسان معتقدند حفظ زنجیره سرد از مرحله تولید تا مصرف، یکی از مهم‌ترین عوامل حفظ کیفیت و سلامت محصولات محسوب می‌شود.

بررسی فعالان این حوزه نشان می‌دهد استفاده از اتاق‌های یخچالی استاندارد نقش مهمی در کاهش ضایعات، افزایش ماندگاری محصولات و کاهش هزینه‌های عملیاتی دارد. به همین دلیل بسیاری از صاحبان کسب‌وکارها به دنبال خرید اتاق یخچالی باکیفیت و استفاده از تجهیزات حرفه‌ای هستند.

یکی از مهم‌ترین فاکتورها در عملکرد مناسب اتاق یخچالی، کیفیت عایق‌بندی است. متخصصان این صنعت استفاده از فوم پلی‌یورتان و بدنه‌های مقاوم فایبرگلاس را از مؤثرترین راهکارها برای حفظ دمای داخلی معرفی می‌کنند.

همچنین نیاز بازار تنها محدود به خودروهای سنگین نیست و امروزه تقاضا برای یخچال نیسان، یخچال مزدا و سایر خودروهای سبک نیز افزایش یافته است. این موضوع باعث شده تولیدکنندگان داخلی ظرفیت تولید خود را توسعه دهند.

در میان شرکت‌های فعال این حوزه، شاهان دژ به عنوان تولیدکننده اتاق یخچالی برای خودروهای سبک، نیمه‌سنگین و سنگین فعالیت می‌کند. این مجموعه از سال ۱۳۹۵ فعالیت خود را آغاز کرده و با بهره‌گیری از تجربه فنی در زمینه ساخت اتاق یخچالی، محصولات خود را برای کاربردهای مختلف حمل‌ونقل سردخانه‌ای عرضه می‌کند.

کارشناسان معتقدند با ادامه روند توسعه صنایع غذایی و دارویی، بازار تجهیزات سردخانه‌ای و اتاق‌های یخچالی در سال‌های آینده رشد قابل توجهی را تجربه خواهد کرد.

15 July 2026

From tonight's courthouse crit: Brassaï's Sculpture involontaire (1932), Richard Dadd, ignoring and then acknowledging the negative ghost of the positive image, marquetry (with respect to Airframe), flag design/art, witnessing on three different levels (seeing the fence, the experience of seeing the fence, and the painting itself), Hilma af Klint again, one sees the world once as a child and the rest of life is memory of that seeing, This Country (2017-2020), accumulated masses cohered, fortune fish, offering versus showing.

Every so often an essay refuses to become what you thought it was. For the last few days I have been working on a piece tentatively titled The Espadrilles. On the surface it is a simple story. A lifelong vegetarian travels into the hills east of Tardets to visit a traditional espadrille workshop in search of a pair of shoes that might sit a little more comfortably with his convictions.

I thought I understood the essay before I wrote it. I didn't.

Writing it has been unexpectedly difficult. Not because I lacked the material, but because every time I thought I understood what the piece was about, it shifted beneath me. At first I thought it was about contradiction. The contradiction is obvious enough. I have been vegetarian for fifty years. I have spent much of my life trying to align my conduct with my convictions. Yet I wear leather shoes. Every vegetarian knows the conversation. Every vegetarian knows the smile. Every vegetarian knows the moment someone points triumphantly at a belt, a wallet or a pair of shoes and says, “That's a bit contradictory.” They're right. It is.

But as I wrote, the essay moved elsewhere. The workshop itself began to take on a life of its own. The hemp. The rope. The old machinery. The smell of oil and dust. The stories of migration and craft. Most importantly, the artisan who welcomed me into his world and revealed his values through the way he handled materials, tools and traditions.

Gradually I realised I was no longer writing about shoes. I was writing about recognition. About finding myself in the presence of a way of life that seemed to embody many of the things I value: care, craftsmanship, hospitality, continuity, attention and pride in work. Then came the moment that gave the essay its tension. After hours spent discussing hemp, rope and traditional manufacture, the artisan unveiled what he regarded as the finest expression of his craft. Leather. Beautiful leather. Locally produced leather. Some of Navarre's finest leather. The obvious reading is contradiction. The vegetarian encounters leather.

But that wasn't what stopped me. What stopped me was the sudden recognition that the artisan and I shared many of the same values, yet arrived at different conclusions about what those values demanded. For him, the leather was not compromise. It was excellence. It was an act of generosity. It was hospitality. The finest thing he knew how to offer another person whom he believed genuinely appreciated his craft.

That realisation opened a much larger question than the one with which I began. For years I have been interested in the relationship between values, politics, participation, community and consensus. Like many people, I suspect, I carried an unexamined assumption that sufficiently good people, sharing sufficiently good values, would naturally tend towards agreement. The workshop revealed a crack in that assumption.

Two people may share values and still arrive at different conclusions about what those values demand. Goodness does not automatically generate consensus. I am still trying to understand what follows from that observation. The essay is therefore unfinished. Not abandoned. Simply unfinished.

I now know where it wishes to go, but I also know that rushing it would do it a disservice. The workshop, the artisan, the swallows, the coffee and the leather have revealed something I did not see when I began. For now, that is enough.

The draft remains on my phone. I suspect I will revisit it many times over the coming months. Not because I need to finish the story, but because I want to understand more clearly what the story has already revealed.

Sometimes an essay's first duty is not to reveal something to its readers. But, to teach its author. For the moment, The Espadrilles has done precisely that. David Marshall Skerries

Excellent read.

The Marshall Review can now be found directly at https://rvw.ie.

The new address is shorter, easier to type and easier to share, but nothing else has changed. The journal's essays, reviews and ongoing series will continue to appear as before.

Existing links to review.marshall.ie will continue to work and will redirect automatically to the current site.

I write for readers. Whether you arrive regularly or happen upon an article by chance, your visit is appreciated. I hope you'll find a reason to return to rvw.ie from time to time. You're always welcome here.

David Marshall Dublin

I need to go to sleep so that I can be properly rested for my date tomorrow, but I guess I just wanted to mention how I was talking with J And I mentioned how if I wanted to be like unreasonable and say if she wanted to she would, there was a specific thing she could offer to do, but that was like a joke because I thought it was way too unreasonable to expect that out of someone, and when I looked at my text messages she had offered that. In the grand scheme of things it’s not like anything mind-boggling, I guess, but very much made me feel. Like I was valued and respected, and that she kind of reciprocated effort which matters so much to me. We also talked about some things about trust and how much our word means to us, and we’re both aligned there. I’m really optimistic. But at the same time, if it doesn’t work, I will live and I want to remind myself of that because I want to actively choose this person not just feel like I need a relationship and so I will take it.

The 500th milestone is still far away but I've got to get going with plans if I ever have a hope of reaching it. I may have to reach my 500th milestone by a combination of active and passive income. I don't think I'm entirely willing to devote time and energy to get a job that pays 500k per annum (and if stress doesn't get me, maybe impostor syndrome will). So here I am, taking stock of my income and expenses:

• My day job pays under 100k per year and this is all I consider to be my source of income at the moment.
• I have some shares outside of super and they are on a dividend re-investment plan. I also have no plans of selling these shares.
• I have a mortgage on a one-bedroom apartment that I bought in May this year, so I'm still clawing equity on it. I used the first home buyer super saver scheme to help me save and I really liked having the automatic deductions from my fortnightly pay (I would have spent the money otherwise).
• I spent alot of my cash on hand buying the apartment so I'm also still trying to pad out my savings again.

I don't have a partner, I have no dependents or pets, and I don't have traditional vices. I do have expensive hobbies. I travel all around the east coast of Australia alot. I also want to get a recreational pilot's license (my goal for the 30s).

Anyway, the only feasible passive income sources for my capabilities (that I can think of) is dividends and royalties if I ever decide to publish a fanfic adjacent piece of lore (fanfic is for another post).

Perhaps a 2-monthly automatic purchase of dividend focused exchange traded funds will be a start. I will put them all on a dividend re-investment plan while I'm in a gathering phase. I will report back at some point when I figure out how to automate the purchases. It needs to be automated or I will be tempted to spend the money.

Meine Steuererklärung scheitert selten daran, dass ich keine Zeit dafür hätte. Meist scheitert sie zunächst daran, dass ich keine Lust verspüre, mich einer Aufgabe zu unterwerfen, deren unmittelbaren Nutzen ich nicht erkenne. Ähnlich geht es mir bei Behördenschreiben oder administrativen Pflichten, die von aussen an mich herangetragen werden.

Mein Verhaltensmuster ist ziemlich zuverlässig: Ich verweigere zunächst den Beginn und erledige stattdessen demonstrativ etwas anderes. Natürlich handelt es sich dabei nicht um irgendeine Ablenkung, sondern um etwas vermeintlich Wichtiges. Ich beantworte E-Mails, ordne Unterlagen oder erledige eine Aufgabe, die ebenfalls schon länger auf meiner Liste steht. So kann ich mir einreden, produktiv zu sein. Erst wenn die Frist näher rückt und der äussere Druck gross genug wird, widme ich mich der ursprünglichen Aufgabe.

Lange hätte ich dieses Verhalten als mangelnde Disziplin bezeichnet. In der Typologie des Sozialwissenschaftlers Itamar Shatz [1] finde ich jedoch eine passendere Erklärung: Ich erkenne mich im „Rebellen“ wieder. Dieser schiebt Aufgaben nicht einfach aus Bequemlichkeit auf. Er wehrt sich gegen Fremdbestimmung und versucht, sich durch das Aufschieben ein Stück Autonomie zurückzuholen.

Der Rebell ist allerdings nur einer von neun Prokrastinationstypen, die Shatz unterscheidet. Dahinter steht eine wichtige Erkenntnis: Es gibt nicht den Prokrastinierer. Menschen schieben aus unterschiedlichen Gründen auf und benötigen entsprechend unterschiedliche Gegenstrategien.

Kein Mangel an Willenskraft

In meinem ersten Beitrag zum Thema habe ich #Prokrastination als freiwilliges Verzögern einer Aufgabe trotz absehbarer negativer Konsequenzen beschrieben. Entscheidend ist die Abgrenzung zum bewussten Aufschieben. Wer eine Aufgabe verschiebt, weil noch Informationen fehlen oder weil ein späterer Zeitpunkt tatsächlich günstiger ist, prokrastiniert nicht. Von Prokrastination sprechen wir, wenn wir wissen, dass uns die Verzögerung schadet, und trotzdem nicht handeln.

Häufig wird dieses Verhalten auf schlechte Planung, fehlende Motivation oder zu wenig Willenskraft zurückgeführt. Shatz hält das für eine Verwechslung von Ursache und Symptom. Hinter dem Aufschieben können Angst, Erschöpfung, Perfektionismus, geringe Erfolgserwartungen, Ablenkbarkeit oder ein Konflikt mit äusseren Erwartungen stehen. Derselbe Mensch kann je nach Aufgabe aus ganz unterschiedlichen Gründen prokrastinieren [2].

Das erklärt auch, weshalb allgemeine Produktivitätstipps so unzuverlässig funktionieren. Eine strengere Deadline kann einem leicht ablenkbaren Menschen helfen. Bei jemandem, der sich gegen äussere Kontrolle wehrt, verstärkt sie möglicherweise den Widerstand. Erholung ist für einen erschöpften Menschen notwendig. Für jemanden, der vor allem das unmittelbar Angenehme sucht, kann sie hingegen zur nächsten Ausweichhandlung werden.

In meinem zweiten Beitrag habe ich mit Skinners Gesetz einen Ansatz vorgestellt, der die Freude am Handeln erhöht oder die Kosten des Nichtstuns vergrössert. Auch solche Anreize können nützlich sein. Sie greifen aber zu kurz, wenn die eigentliche Ursache nicht erkannt wird.

Neun Typen der Prokrastination

Shatz unterscheidet neun typische Muster. Sie sind nicht als wissenschaftliche Diagnosen oder unveränderliche Persönlichkeitstypen zu verstehen. Vielmehr bieten sie ein Raster, um genauer zu beobachten, was hinter dem eigenen Aufschieben steckt.

Typ	Typisches Muster	Mögliche Ursache	Passender Umgang
Besorgter	Beginnt nicht, weil etwas schiefgehen könnte	Angst vor negativen Konsequenzen oder Bewertung von aussen	Befürchtung konkret benennen, ihre Wahrscheinlichkeit prüfen und mit einem kleinen Schritt beginnen
Erschöpfter	Fühlt sich zu müde oder ausgelaugt für die Aufgabe	Überlastung oder Stress	Erholung priorisieren, Anforderungen überprüfen und unrealistische Ziele reduzieren
Perfektionist	Beginnt spät oder wird nie fertig	Überhöhter eigener Anspruch, das Ergebnis genügt nie ganz	Vorab festlegen, was «gut genug» bedeutet, und eine unfertige erste Version zulassen
Pessimist	Erwartet, ohnehin keinen Erfolg zu haben	Geringe Selbstwirksamkeit und starke Selbstkritik	Annahmen überprüfen und sich so beraten, wie man einen Freund beraten würde
Träumer	Denkt gerne über Ziele nach, setzt sie aber kaum um	Die Vorstellung der Zukunft ist attraktiver als die konkrete Arbeit	Wünsche in beobachtbare Handlungen, Termine und nächste Schritte übersetzen
Zickzack-Typ	Springt laufend zwischen Aufgaben und Reizen	Ablenkbarkeit, fehlende Prioritäten oder eine reizreiche Umgebung	Ein Ziel schriftlich festhalten, Ablenkungen entfernen und die Aufgabe in Schritte zerlegen
Rebell	Widersetzt sich vor allem fremden Vorgaben	Bedürfnis nach Autonomie und Kontrolle	Eigenen Nutzen klären und innerhalb der Aufgabe echte Wahlmöglichkeiten schaffen
Adrenalin-Sucher	Arbeitet erst kurz vor Ablauf der Frist	Aktivierung und Spannung durch Zeitdruck	Zwischenfristen setzen und die Kosten des hektischen Endspurts ehrlich bilanzieren
Hedonist	Entscheidet sich für das, was sich jetzt besser anfühlt	Unmittelbare Belohnungen wiegen stärker als spätere Vorteile	Versuchungen erschweren und die Aufgabe mit einer zeitnahen kleinen Belohnung verbinden

Die Übersicht macht deutlich, dass eine identische Handlung sehr verschiedene Hintergründe haben kann. Zwei Personen reichen ihre Unterlagen erst am letzten Tag ein. Die eine fürchtet, einen Fehler zu machen, und kontrolliert jedes Detail mehrfach. Die andere empfindet die Aufgabe als fremdbestimmt und beginnt aus Widerstand nicht. Eine gemeinsame Deadline bedeutet noch keine gemeinsame Ursache.

Wie sich eine Ursache bei mir konkret zeigt: der Rebell und die Illusion der Kontrolle

Beim Rebellen erfüllt die Prokrastination eine besondere Funktion. Das Aufschieben vermittelt kurzfristig das Gefühl, sich einer Anordnung nicht vollständig zu unterwerfen. Ich entscheide schliesslich selbst, wann ich die Steuererklärung ausfülle. Indem ich zuerst andere Aufgaben erledige, stelle ich symbolisch meine eigene Prioritätenordnung wieder her. Das Problem liegt im Ergebnis. Was sich zunächst wie Selbstbestimmung anfühlt, führt zu immer stärkerer Fremdbestimmung. Die Frist rückt näher, der Handlungsspielraum schrumpft, und am Ende diktiert der Zeitdruck, wann und unter welchen Bedingungen ich arbeiten muss. Der Rebell verschafft sich durch das Aufschieben kurzfristig ein Gefühl von Kontrolle und verliert dadurch langfristig umso mehr davon.

Mehr Druck ist deshalb nicht unbedingt die beste Antwort. Hilfreicher ist es, den eigenen Nutzen der Aufgabe zu klären. Eine Steuererklärung bleibt eine Pflicht. Ich kann sie aber als Voraussetzung betrachten, um finanzielle Angelegenheiten abzuschliessen, Unsicherheit zu beseitigen und anschliessend wieder über meine Zeit zu verfügen. Ebenso kann ich mir innerhalb der Aufgabe Wahlmöglichkeiten schaffen: Wann erledige ich sie? In welcher Reihenfolge gehe ich vor? Welche Unterlagen bereite ich zuerst vor? Hole ich Unterstützung oder arbeite ich allein? Die Aufgabe wird dadurch nicht angenehmer. Sie erscheint aber weniger als reiner Gehorsamsakt.

Erst die Ursache, dann die Methode

Die neun Typen erklären, weshalb es keine universelle Methode gegen Prokrastination gibt. “Fang einfach an” kann dem Besorgten helfen, sofern der erste Schritt klein genug ist. Einem tatsächlich erschöpften Menschen vermittelt derselbe Rat möglicherweise nur, er müsse seine Grenzen ignorieren. Eine öffentliche Verpflichtung kann für den Zickzack-Typ eine sinnvolle Struktur schaffen. Beim Rebellen kann sie zusätzlichen Widerstand auslösen.

Vor der Wahl einer Methode steht deshalb eine genauere Diagnose des eigenen Verhaltens. Dabei helfen drei Fragen:

1. Was vermeide ich bei dieser Aufgabe konkret?
2. Welches Gefühl verschwindet kurzfristig, wenn ich sie aufschiebe?
3. Welche Veränderung würde genau diese Hürde verkleinern?

Manchmal lautet die Antwort Angst vor einem schlechten Ergebnis. Manchmal fehlt ein klarer erster Schritt. Vielleicht ist die Aufgabe tatsächlich unnötig, schlecht definiert oder unter den gegenwärtigen Bedingungen kaum zu bewältigen. Nicht jede Abneigung ist ein psychologisches Problem, das mit besserem #Selbstmanagement gelöst werden muss.

Nützliche Orientierung statt festes Etikett

Die Typologie von Shatz vereinfacht natürlich komplexes Verhalten. Menschen lassen sich kaum dauerhaft einer einzigen Kategorie zuordnen. Bei administrativen Pflichten kann ich als Rebell reagieren, beim Schreiben eines wichtigen Textes dagegen perfektionistische Züge zeigen. Erschöpfung kann Ablenkbarkeit verstärken, während Pessimismus und Angst häufig gemeinsam auftreten.

Der Wert der Typologie liegt daher nicht darin, sich selbst ein neues Etikett zu geben. Sie stellt bessere Fragen bereit. Statt mich pauschal als undiszipliniert zu beurteilen, kann ich untersuchen, welche Funktion das Aufschieben in einer konkreten Situation erfüllt. Das ist weniger moralisch, aber anspruchsvoller: Eine unpassende Gegenstrategie lässt sich leicht anwenden. Die tatsächliche Ursache zu erkennen, verlangt ehrliche Selbstbeobachtung.

Meine Steuererklärung wird durch diese Erkenntnis weder interessanter noch schneller erledigbar. Ich muss sie weiterhin ausfüllen. Ich kann jedoch darauf verzichten, mich zunächst wegen mangelnder Willenskraft zu verurteilen und anschliessend noch mehr Druck aufzubauen. Sinnvoller ist es, meinen Widerstand als Hinweis zu verstehen und die Aufgabe so weit wie möglich wieder zu meiner eigenen zu machen.

Die entscheidende Frage lautet deshalb nicht: wie diszipliniere ich mich besser? Sie lautet: warum schiebe ich genau diese Aufgabe auf?

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💬 Kommentieren (nur für write.as-Accounts)

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Fussnoten [1] R. Blakely, «Zigzagger, dreamer or rebel: what kind of procrastinator are you?», The Times, 10. Juli 2026. [Online]. Verfügbar: https://www.thetimes.com/uk/science/article/what-kind-of-procrastinator-are-you-cmqhd7zfq [2] I. Shatz, «Why People Procrastinate: The Psychology and Causes of Procrastination», Solving Procrastination. [Online]. Verfügbar: https://solvingprocrastination.com/why-people-procrastinate/

Bildquelle Friedrich Wilhelm von Schadow und Schüler (1788–1862): Hölle (rechter Teil des Triptychons Fegfeuer – Paradies – Hölle), Museum Kunstpalast, Düsseldorf Public Domain.

Disclaimer Teile dieses Texts wurden mit Deepl Write (Korrektorat und Lektorat) überarbeitet. Für die Recherche in den erwähnten Werken/Quellen und in meinen Notizen wurde NotebookLM von Google verwendet.

Topic #Coaching | #ProductivityPorn

I canceled my plans today. All of them. I do feel sick, but also I don’t feel like I have energy to meet up with people. Not yet, I want to meet up with friends but it feels overwhelming for some reason. I rescheduled everything for next week so hopefully these feelings go away by then. Soon enough, my friends may get angry, or worse, worried.

My mom eventually forced me to get out of the house for just a bit and run an errand with her. I do still need to make a list and a schedule so I feel a little more on top of things. This is kind of making me realize I may not be able to run my own business when I can’t even manage my own time off right now.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

There is a particular kind of confidence that radiates from a screen. A clinician holds a dermatoscope against a patient's skin, captures the image, and a number appears: a probability, a risk score, a clean computational verdict rendered in the universal language of decimals. The machine does not hesitate. It does not say “I am less sure about this one.” It returns the same crisp output whether the skin beneath the lens is the pale, freckled forearm of a redhead from the Scottish Highlands or the deep brown shoulder of a man whose ancestry traces to West Africa. The interface is identical. The confidence is identical. The accuracy, it turns out, is not.

This is the uncomfortable fact at the centre of a slow-building reckoning in one of medicine's most visual specialties. Dermatology was supposed to be the field where artificial intelligence would shine first and brightest. Skin is, after all, the organ you can photograph. A lesion sits on the surface, available to any camera, ready to be classified by a neural network trained on hundreds of thousands of examples. The promise was seductive: democratised expertise, faster triage, melanomas caught months earlier, lives saved in places where a dermatologist is a four-hour drive and a six-month waiting list away. And much of that promise is real. But woven through the optimism is a structural flaw that the field has known about, documented, quantified, and only partially addressed. The machines see darker skin less well. And the institutions deploying them have, for the most part, not told the patients standing on the wrong side of that gap.

The question this raises is not merely technical. It is a question about what we owe people when we ask them to trust a tool we know to be unequal. If an AI diagnostic system is understood by its makers and its deployers to perform worse on darker skin, and it is used on a patient with darker skin who is never told this, has that patient truly consented to anything at all? And what does the principle of health equity, so often invoked and so rarely operationalised, actually demand of the hospital, the clinic, or the national health service that flips the switch?

A Specialty Where Looking Is Everything

To understand why bias in dermatology AI is so consequential, you have to understand the stakes of the underlying diagnosis. Melanoma is the deadliest of the common skin cancers, and it is almost uniquely sensitive to timing. Caught early, while it is still confined to the upper layers of the skin, it is among the most survivable of all cancers. The American Cancer Society puts the five-year survival rate for localised melanoma at around 99 per cent. Allow it to metastasise, to spread to distant organs, and that figure collapses to roughly a third. Few diagnoses in medicine carry such a steep cliff between early and late, between a minor excision under local anaesthetic and a death sentence delivered in instalments.

That cliff does not fall equally across the population. The data on racial disparities in melanoma outcomes is stark and long-established. For the period from 2015 to 2021, the five-year melanoma survival rate among white Americans was about 95 per cent. Among Black Americans, it was roughly 70 per cent. The gap is not driven by biology in any simple sense; melanoma is, in absolute terms, rarer in people with darker skin. It is driven overwhelmingly by stage at diagnosis. One widely cited figure holds that around 39 per cent of Black patients present with regional or distant disease, stage III or stage IV, compared with roughly 15 per cent of white patients. By the time the cancer is found, it has often already moved.

This is the world into which dermatology AI arrives: a specialty where the central task is recognition, where the difference between treatable and fatal is measured in how early something is seen, and where the populations whose cancers are already being caught too late are precisely the populations most likely to be poorly served by a tool that struggles to see them. A technology that performs unequally across skin tones does not enter a level field. It enters a field already tilted, and it risks tilting it further.

The Number That Changed the Conversation

For years, the underperformance of dermatology algorithms on darker skin was suspected, asserted, and worried over, but rarely measured with the kind of rigour that compels institutional attention. The problem was partly circular: to test how an algorithm performs across skin tones, you need a high-quality dataset that spans skin tones, with diagnoses confirmed not by a clinician's guess but by the gold standard of biopsy. Such a dataset did not exist. The very gap in the training data made the gap in performance hard to prove.

That changed in 2022, when a team led by researchers at Stanford, including Roxana Daneshjou and Albert Chiou, published a study in Science Advances built around a resource they had assembled called Diverse Dermatology Images, or DDI. It was, they noted, the first publicly available, expertly curated, pathologically confirmed image set deliberately balanced across the full range of skin tones. The numbers behind it are worth stating plainly, because their plainness is the point. The dataset contained 656 images from 570 patients, sorted by Fitzpatrick skin type: 208 images of the lightest skin, types I and II; 241 of the middle range, types III and IV; and 207 of the darkest skin, types V and VI. Crucially, every lesion had been biopsied, so the truth of each diagnosis was not in question.

When the researchers ran state-of-the-art dermatology algorithms against this honest benchmark, the results were sobering. The models' ability to distinguish malignant from benign lesions, measured by an area under the curve, dropped sharply when confronted with images they had not been built to handle. Performance fell by figures in the region of 27 to 36 per cent relative to the algorithms' own published results, and the decline was concentrated, predictably, on darker skin and on rarer diseases. These were not obscure or amateurish systems. They were among the best in the field, the kind of models that generate excited headlines about machines outperforming doctors. Tested fairly, they faltered exactly where the human cost of faltering is highest.

The study did not end on despair, and this matters for the ethics that follow. When the team fine-tuned the algorithms on the diverse DDI images, the gap narrowed and in places vanished. Models retrained on darker skin not only closed the distance between light and dark performance but, in the case of malignancy detection on dark skin, outperformed the dermatologist raters used for comparison. The lesson was unambiguous. The bias was not an inevitable property of the technology. It was a property of the data, and data can be changed. The disparity was a choice, even if no one had consciously chosen it.

A Structural Problem, Not a Bug

If the DDI study supplied the hard number, a review published in June 2024 in the journal Frontiers in Artificial Intelligence supplied the diagnosis of the disease behind it. Written by Nazma Khatun, Gabriella Spinelli, and Federico Colecchia, the paper set out to map the landscape of technology aimed at reducing health inequality in skin diagnosis for people of colour, and it reached a conclusion that should unsettle anyone who imagines the problem can be patched with a software update.

The authors documented the human cost in unsparing terms, citing evidence that African Americans are around four times more likely to present with stage IV melanoma owing to delayed diagnosis, and approximately 1.5 times more likely to die of the disease than white patients, with five-year survival rates they put at 72.2 per cent against 89.6 per cent. Then they turned to the machinery meant to help. What they found in the training data was not a marginal shortfall but a near-absence. Studies that claimed to include people of colour, they noted, frequently included almost none. One prominent example involved a dataset in which just 2.7 per cent of participants were Fitzpatrick type V and not a single one was type VI, the very darkest category. In another instance, an algorithm reporting impressive accuracy in development correctly diagnosed only a small fraction of cases when tested against predominantly darker skin.

The review's central argument was that this was structural. The underrepresentation of people of colour in dermatology AI was not a discrete error introduced at one point in the pipeline that could be excised by a diligent engineer. It was the downstream consequence of a chain of older inequities: medical curricula that taught skin disease almost exclusively on white skin, research cohorts that skewed white, clinical photography archives accumulated in institutions serving largely white populations, and a development culture that treated representativeness as a nice-to-have rather than a precondition. Without intervention upstream, the authors warned, the systemic underrepresentation could not be solved and would only amplify the disparities already baked into care. The machines were not inventing bias. They were inheriting it, encoding it, and scaling it.

The Datasets That Will Not Say What They Contain

Here the story takes a turn that sharpens the consent question to a fine point. Even if a clinician wanted to know how a given AI tool would perform on a given patient, she frequently could not find out, because the datasets underlying these tools often do not record the one variable that matters most.

Consider the public benchmarks that dominate the field. The ISIC archive and the widely used HAM10000 dataset are foundational resources, the raw material on which a great deal of dermatology AI has been built. Analyses of these collections have repeatedly found them overwhelmingly composed of lighter skin. One assessment of the large ISIC 2020 collection and the related MILK10k set estimated that fewer than one per cent of subjects fell into the darkest Fitzpatrick categories, with the data dominated by lighter types. A benchmark with that composition cannot tell you how a model behaves on dark skin, for the simple reason that dark skin is barely present to be measured. The numbers that look reassuring in a published table describe a population that does not include the patient in front of you.

The deeper problem, surfaced in a study published in npj Digital Medicine in November 2025 by Yingjoy Li, Veronica Rotemberg, Roxana Daneshjou, Jenna Lester, and colleagues, is that many datasets do not document their skin tone composition at all. The team proposed a tool they called a Dataset Nutrition Label, a structured summary of a dataset's contents and limitations modelled loosely on the nutritional information panel on packaged food. Applying it to a large 2024 dataset of more than 400,000 lesion images drawn from total body photography, they found that it contained no skin tone documentation whatsoever. Their label flagged the omission explicitly, cautioning against deploying models trained on the data to assess individuals with darker skin and warning of hidden proxies and underrepresented populations lurking unmeasured within.

Sit with what this means at the bedside. A clinician adopting such a tool cannot consult the label, because there is no label. She cannot reason about her patient population, because the composition of the training data is undisclosed. She inherits a system whose performance on the person in her chair is, in the most literal sense, unknown and unknowable from the documentation provided. The transparency that informed consent presupposes, the idea that someone in the chain knows the relevant facts and can convey them, breaks at the source. You cannot disclose what was never recorded.

The Measuring Stick Is Also Broken

It would be convenient if the tool we use to describe skin tone were itself sound. It is not, and this is more than a pedantic footnote. The Fitzpatrick scale, the six-category system that pervades dermatology and structures nearly every dataset described above, was never designed to measure skin colour. It was devised in the 1970s to predict how skin would respond to ultraviolet light, how readily it would burn and how readily it would tan. It was, in origin, a sunburn classifier built around the responses of lighter skin, later extended to cover darker types almost as an afterthought.

A study published in npj Digital Medicine in December 2025 by Victoria Weir, Veronica Rotemberg, and colleagues compared the Fitzpatrick scale against alternatives, including the more recent Monk Skin Tone scale and objective colorimetry. The Fitzpatrick categories, they found, showed the weakest clustering when mapped against measured colour, meaning each Fitzpatrick band sprawled across a wide and overlapping range of actual skin tones. The scale, the authors observed, does not measure skin tone; it measures photosensitivity, and the two are related but not the same. The Monk scale and objective colour measurement both performed better, with the Monk scale in particular proving more reliable and more capable of revealing genuine differences in how melanoma algorithms perform across tones.

The implication compounds every problem already described. The field has been auditing its own fairness using a ruler with blurred and arbitrary markings, originally manufactured to answer a different question entirely. When a dataset reports its Fitzpatrick distribution, it is offering a coarse, contested proxy and calling it a measurement. The instrument of accountability is itself part of what needs reforming.

What Consent Was Supposed to Mean

Step back from the technical thicket and the ethical architecture comes into focus. Informed consent is one of the load-bearing pillars of modern medicine, the legal and moral mechanism by which a patient's body remains their own even as they hand themselves over to expert care. Its logic is that a competent adult is entitled to the information a reasonable person would want in order to decide whether to accept a proposed course of action, including its material risks and reasonable alternatives. The patient need not become a physician. But they are owed the facts that would matter to a sensible person weighing the choice.

The legal scholarship on whether this doctrine reaches the use of artificial intelligence is, as yet, cautious. In an analysis of AI and the law of informed consent, the legal scholars I. Glenn Cohen and Andrew Slottje concluded in 2024 that current United States law probably does not require a physician to disclose, as a general matter, that an AI system was involved in a diagnosis. The reasoning runs through the materiality standard. Under the patient-centred version of that standard, a risk must be disclosed when a reasonable person would attach significance to it in deciding on treatment. The mere fact that software assisted a clinician, the argument goes, may be no more material than the fact that the clinician consulted a textbook or a colleague.

But Cohen and Slottje also identified the precise circumstance in which the analysis shifts, and it is the circumstance this entire article describes. Algorithmic bias, they noted, can be material, particularly where training data underrepresents a patient's group in a way that predicts poorer performance for that patient specifically. That is not a textbook the clinician happened to read. That is a known, quantified, group-specific reduction in the reliability of the very tool being used to decide whether a mark on someone's skin is cancer. It is difficult to imagine a fact a reasonable patient would more plainly want to know. The commentator Emma Kondrup, writing for Harvard's Petrie-Flom Center in April 2025, pressed the broader worry that informed consent in the age of opaque, evolving algorithms risks becoming symbolic, a signature collected on a form for a process the patient cannot meaningfully evaluate. When the relevant risk is not the inscrutable inner workings of a black box but something as concrete as “this tool was tested mostly on skin lighter than yours and is known to be less accurate on skin like yours,” symbolism is not good enough.

The consent question, then, resolves into something quite sharp. Consent that conceals a material, group-specific disparity is not consent in any meaningful sense. It is the form of consent without its substance, a ritual that produces a signature while withholding the one fact that might have changed the signer's mind. And the cruelty of the arrangement is its distribution. The patients on the wrong side of the accuracy gap are disproportionately those who, in many health systems, have the least access to a specialist second opinion, the fewest resources to seek out alternative assessment, and the least standing to contest a diagnosis that arrives late. The tool performs worst for the people least equipped to notice or to challenge its failure. A disparity in accuracy lands on top of a disparity in power, and the two reinforce each other.

The British Experiment in Doing It Differently

If this all sounds abstract, Britain offers a concrete and instructive case, because the question of deploying biased dermatology AI is not hypothetical there. It is operational. An AI system called DERM, developed by the company Skin Analytics, has been used across a number of NHS England trusts to assess skin lesions, in some configurations taking patients off the urgent cancer pathway without a doctor reviewing every benign result. In May 2025, the National Institute for Health and Care Excellence, the body that judges which technologies the NHS should adopt, issued a conditional recommendation: DERM could be used within the health service over a three-year evidence-generation period while its real-world value was assessed.

What makes the NICE decision notable for the consent debate is what it did about skin tone. Rather than wave the technology through with uniform confidence, NICE built the known uncertainty into the rules of deployment. It specified that for patients with black or brown skin, an additional healthcare professional review would take place during the evidence period, reflecting that the evidence for the tool's accuracy in those groups was less certain. The institution, in other words, did not pretend the disparity away. It acknowledged that it did not yet know how well the machine saw darker skin, and it placed a human safeguard precisely where the machine was least trustworthy.

This is, in one reading, exactly what health equity ought to require: an institution confronting a known performance gap not by hiding it but by compensating for it, allocating extra scrutiny to the patients the technology is most likely to fail. Yet it also lays the underlying problem bare. The British Association of Dermatologists has voiced the longstanding worry that the underrepresentation of darker skin in image datasets could cause AI to perform poorly on those patients, and has noted how few data exist on the technology's effectiveness on dark skin. NICE's safeguard is a tacit admission that the tool is being deployed before that uncertainty is resolved. The human second read is a patch over a gap that more representative data should have closed years earlier. It is a humane response to an inequity that better data collection might have prevented from arising at all.

There is a further, subtler point buried in the NICE arrangement. The extra review for darker skin is a form of institutional disclosure, a recognition encoded in policy that performance differs by skin tone. But the patient sitting in the clinic may never learn why their case is being handled differently, or that it is being handled differently at all. The safeguard protects the patient's body without necessarily informing the patient's mind. It is better than nothing, considerably better, but it is not yet the full transparency that meaningful consent would demand.

What the Regulators See, and What They Do Not Require

Regulators on both sides of the Atlantic have begun, haltingly, to grapple with this. In the United States, the Food and Drug Administration issued draft guidance in January 2025 on the lifecycle management of AI-enabled medical devices, and its expectations now include analysis of performance across demographic subgroups, with attention to race, ethnicity, age, sex, and the equipment used to capture images. On paper, this is the regulator asking precisely the right question: does the device work for everyone it will be used on?

The gap, as ever, lies between the paper and the practice. Analyses of the transparency actually achieved by FDA-reviewed AI devices have found it wanting. One assessment found that demographic reporting in device summaries, while rising, remained low, with fewer than one in five summaries providing race or ethnicity data, and structured subgroup-level performance reporting largely absent. A substantial share of devices reported no clinical study at all, and many reported no performance metrics of any kind. The regulator is asking for the information that would make informed deployment possible. It is frequently not getting it, and it is clearing devices anyway. The result is a market in which a hospital can lawfully acquire an AI dermatology tool whose performance on darker skin is, from the published record, simply unknown.

This is where the chain of responsibility comes into focus, and where it tends to dissolve. The dataset curators did not record skin tone. The developers trained on what was available and reported what regulators minimally required. The regulators cleared the device against a framework that asks for subgroup data but does not reliably compel it. And the deploying institution acquires a tool wrapped in documentation that does not answer the one question equity demands. At each handoff, the relevant fact, “this may not work as well on darker skin,” can slip through a gap in the floorboards, until it reaches a clinician who has no way to retrieve it and a patient who is never told it existed.

What Equity Actually Requires

It is easy to invoke health equity and hard to say what it concretely obliges. The phrase risks becoming a comfortable abstraction, a value affirmed in mission statements and forgotten at procurement. So let us be specific about what it demands of an institution choosing to deploy a dermatology AI system whose performance across skin tones is unequal or unknown.

First, it demands honesty in acquisition. An institution should not deploy a tool whose skin tone performance it cannot characterise, and where the documentation is silent it should treat that silence not as reassurance but as a red flag. The Dataset Nutrition Label proposed by the npj Digital Medicine researchers exists precisely so that absence can be made visible rather than assumed away. An institution that adopts a tool with no skin tone data has not made a neutral choice. It has made a choice to operate in the dark, and it has chosen on behalf of patients who never agreed to be experimented upon.

Second, it demands disclosure to the patient. If a tool is known to perform less accurately on darker skin, the patient with darker skin is owed that information in terms they can understand, alongside the alternatives available to them, including the option of conventional assessment by a clinician. This is not a demand for a lecture on convolutional neural networks. It is a demand for one plain sentence about a material limitation, the kind of sentence consent doctrine has always required for material risks. The legal floor, as Cohen and Slottje observe, may not yet compel this in most jurisdictions. The ethical ceiling plainly does.

Third, it demands compensating safeguards where disparity is known, of the kind NICE built into the DERM deployment, with extra human review allocated to the patients the technology is most likely to fail. Equity is not achieved by treating everyone identically when the tool itself does not. It is achieved by directing additional protection towards those who would otherwise bear the cost of the tool's weakness.

Fourth, and most fundamentally, it demands investment upstream in the data itself. The DDI study proved that the gap is closable, that fine-tuning on diverse, biopsy-confirmed images can erase the disparity and even surpass human performance on darker skin. The disparity persists not because it is technically intractable but because closing it requires deliberate, funded, sustained effort to collect the images that medicine has historically failed to gather. An institution serious about equity does not merely deploy other people's tools more carefully. It contributes to fixing the foundation, because every clinic that collects diverse, well-documented images is widening the path for the next generation of fairer systems.

The Patient Who Was Never Asked

Return, finally, to the screen and its serene confidence. The machine does not know that the skin beneath the lens is dark. It does not know that the dataset it learned from contained almost no one who looked like this patient. It does not know that its certainty is, in this particular case, partly counterfeit. It simply returns its number, clean and unhesitating, and the number carries an authority it has not earned for this person.

The patient, meanwhile, knows none of this either. They were told, perhaps, that an AI system would help assess their skin, and that sounded modern and reassuring, the hospital investing in the future. They were not told that the future had been built mostly out of skin lighter than theirs. They were not offered the sentence that might have prompted them to ask for a second look. They signed, or nodded, or simply did not object, and in the eyes of the institution that counts as consent. It is consent the way a photograph of a meal is dinner: the shape is right, the substance is missing.

The reckoning underway in dermatology AI is often framed as a problem of data, and at one level it is. But beneath the data sits something older and more demanding, a question about what we owe one another when we build tools that see some people more clearly than others. The studies have done their work. The disparity is measured, the mechanism understood, the remedy demonstrated. What remains is a choice about candour, about whether the institutions wielding these systems will speak the plain truth to the patients most at risk of being failed by them, or whether they will let the machine's borrowed confidence stand in for an honesty they were never quite willing to offer. Consent that hides the thing the patient most needs to know is not a contract. It is a performance. And the people watching it most closely, though they may not yet realise it, are the ones it is least designed to protect.

References

1. Daneshjou, R., Vodrahalli, K., Novoa, R.A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S.M., Bailey, E.E., Gevaert, O., Mukherjee, P., Phung, M., Yekrang, K., Fong, B., Sahasrabudhe, R., Allerup, J.A.C., Okata-Karigane, U., Zou, J., and Chiou, A.S. “Disparities in dermatology AI performance on a diverse, curated clinical image set.” Science Advances, vol. 8, no. 32, 2022. https://www.science.org/doi/10.1126/sciadv.abq6147

2. Khatun, N., Spinelli, G., and Colecchia, F. “Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda.” Frontiers in Artificial Intelligence, 13 June 2024. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1394386/full

3. Li, Y., Taylor, M., Chmielinski, K.S., Halpern, A.C., Daneshjou, R., Lester, J.C., and Rotemberg, V. “Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label.” npj Digital Medicine, 5 November 2025. https://www.nature.com/articles/s41746-025-02125-9

4. Weir, V.R., Li, Y., Gillis, M.C., Kurtansky, N.R., Salvador, T., Halpern, A.C., Nelson, K.C., Lester, J.C., and Rotemberg, V. “Evaluating skin tone scales for dermatologic dataset labeling: a prospective-comparative study.” npj Digital Medicine, 22 December 2025. https://www.nature.com/articles/s41746-025-02245-2

5. Cohen, I.G., and Slottje, A. “Artificial intelligence and the law of informed consent.” In Research Handbook on Health, AI and the Law, 2024. https://www.ncbi.nlm.nih.gov/books/NBK613199/

6. Kondrup, E. “Informed Consent, Redefined: How AI and Big Data Are Changing the Rules.” Petrie-Flom Center, Harvard Law School, 11 April 2025. https://petrieflom.law.harvard.edu/2025/04/11/informed-consent-redefined-how-ai-and-big-data-are-changing-the-rules/

7. American Cancer Society. “Study: Lack of Education About Melanoma May Contribute to Black-White Survival Disparities.” https://www.cancer.org/research/acs-research-news/study-lack-of-education-about-melanoma-may-contribute-to-black-white-survival-disparities.html

8. National Institute for Health and Care Excellence (NICE). “AI skin cancer detection system gets green light for conditional NHS use.” 1 May 2025. https://www.nice.org.uk/news/articles/ai-skin-cancer-detection-system-gets-green-light-for-conditional-nhs-use

9. NHS England. “AI based skin lesion analysis technology.” https://www.england.nhs.uk/elective-care/best-practice-solutions/ai-based-skin-lesion-analysis-technology/

10. British Association of Dermatologists. “Artificial Intelligence.” https://www.bad.org.uk/clinical-services/artificial-intelligence

11. Tschandl, P., Rosendahl, C., and Kittler, H. “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.” Scientific Data, 2018. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6091241/

12. U.S. Food and Drug Administration. “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations.” Draft guidance, January 2025. https://www.fda.gov/media/184856/download

13. “Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices.” npj Digital Medicine, 2025. https://www.nature.com/articles/s41746-025-02052-9

14. Center for Artificial Intelligence in Medicine & Imaging, Stanford University. “DDI – Diverse Dermatology Images.” https://aimi.stanford.edu/datasets/ddi-diverse-dermatology-images

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

WriteFreely v0.17 is here, includes some critical security fixes, along with a ton of quality-of-life improvements and fixes for users.

Download v0.17.0 now, and read on to see what’s new in this version.

Security

• Fix config.ini defaults to world-readable (by @thebaer, CVE-2025-24337)
• Fix Cross-User IDOR: Subscriber List Disclosure (by @thebaer, GHSA-cfcq-76gr-62x8)
• Prevent signups via /auth/signup with closed registrations by @thebaer in #1686

User-Facing Changes

• Title fixes for ActivityPub by @thebaer in #1491
• Fix collection Customize page header nav on single-user instances by @thebaer in #1505
• Fix single-user blog URL on Customize page by @thebaer in #1508
• Add support for alternative smallweb URL schemes by @smazmi in #1565
• Fix font toggle in classic editor #876 by @vtyeh in #1135
• Display stat numbers as monospace by @lolbinarycat in #1142
• Support changing published post font on web by @thebaer in #1609
• Fix character set for post signature column on MySQL / MariaDB instances by @thebaer in #1608
• Redirect /read/feed to correct /read/feed/ URL by @thebaer in #1522
• Fix ProseMirror HTML handling by @thebaer in #1644
• Use datetime picker on Post Metadata page and fix API inconsistency by @thebaer in #1492
• Markdown preview by @thebaer in #1647
• Sort list of Fediverse followers by subscription date, descending by @thebaer in #1630
• Limit password to max characters supported by bcrypt by @thebaer in #1664
• Improve and fix up data exports by @thebaer in #1623
• Fix missing user nav on blog Customize page by @thebaer in #1687

Admin-Facing Changes

• Document MariaDB support in README by @robertsilen in #1665
• Ignore missing socket file on shutdown by @thebaer in #1503
• ActivityPub robustness and monitoring by @blacklight in #1437

Developer-Facing Changes

• Don't add blank strings to mentioned users array in Activity object by @thebaer in #1575
• Refactor: Email Subscription shortcode and form by @thebaer in #1646
• Run goimports on project by @thebaer in #1559

Dependencies and minor fixes

• Add missing hostName assignment in viewEditCollection by @thebaer in #1504
• Bump github.com/stretchr/testify from 1.9.0 to 1.10.0 by @dependabot[bot] in #1151
• Bump docker/metadata-action from 4.6.0 to 5.8.0 by @dependabot[bot] in #1462
• Bump docker/login-action from 3.0.0 to 3.5.0 by @dependabot[bot] in #1463
• Bump docker/build-push-action from 5.0.0 to 6.18.0 by @dependabot[bot] in #1464
• Bump docker/setup-buidx-action from 3.0.0 to 3.11.1 by @dependabot[bot] in #1465
• Bump golang.org/x/net from 0.43.0 to 0.44.0 by @dependabot[bot] in #1500
• Bump docker/login-action from 3.5.0 to 3.6.0 by @dependabot[bot] in #1497
• Bump golang.org/x/crypto from 0.41.0 to 0.42.0 by @dependabot[bot] in #1499
• Bump golang.org/x/net from 0.45.0 to 0.46.0 by @dependabot[bot] in #1509
• Bump golang.org/x/crypto from 0.43.0 to 0.45.0 by @dependabot[bot] in #1528
• Bump golang.org/x/net from 0.47.0 to 0.48.0 by @dependabot[bot] in #1548
• Bump golang.org/x/crypto from 0.45.0 to 0.46.0 by @dependabot[bot] in #1546
• Bump github.com/fatih/color from 1.17.0 to 1.18.0 by @dependabot[bot] in #1140
• Bump github.com/gosimple/slug from 1.14.0 to 1.15.0 by @dependabot[bot] in #1232
• Bump golang.org/x/net from 0.48.0 to 0.49.0 by @dependabot[bot] in #1571
• Bump docker/login-action from 3.6.0 to 3.7.0 by @dependabot[bot] in #1567
• Bump docker/setup-buildx-action from 3.11.1 to 3.12.0 by @dependabot[bot] in #1541
• Bump docker/metadata-action from 5.8.0 to 5.10.0 by @dependabot[bot] in #1525
• Bump docker/setup-qemu-action from 3.0.0 to 3.7.0 by @dependabot[bot] in #1515
• Bump docker/build-push-action from 6.18.0 to 6.19.2 by @dependabot[bot] in #1602
• Bump golang.org/x/net from 0.49.0 to 0.51.0 by @dependabot[bot] in #1627
• Bump docker/login-action from 3.7.0 to 4.0.0 by @dependabot[bot] in #1631
• Bump docker/setup-qemu-action from 3.7.0 to 4.0.0 by @dependabot[bot] in #1632
• Bump docker/metadata-action from 5.10.0 to 6.0.0 by @dependabot[bot] in #1633
• Bump docker/build-push-action from 6.19.2 to 7.0.0 by @dependabot[bot] in #1634
• Bump golang.org/x/net from 0.51.0 to 0.53.0 by @dependabot[bot] in #1657
• Bump github.com/fatih/color from 1.18.0 to 1.19.0 by @dependabot[bot] in #1641
• Bump golang.org/x/crypto from 0.50.0 to 0.51.0 by @dependabot[bot] in #1653
• Bump golang.org/x/net from 0.53.0 to 0.55.0 by @dependabot[bot] in #1673
• Bump golang.org/x/crypto from 0.51.0 to 0.53.0 by @dependabot[bot] in #1674
• Bump docker/setup-buildx-action from 3.12.0 to 4.1.0 by @dependabot[bot] in #1669
• Bump actions/checkout from 4 to 7 by @dependabot[bot] in #1684
• Bump docker/build-push-action from 7.0.0 to 7.2.0 by @dependabot[bot] in #1666
• Bump docker/metadata-action from 6.0.0 to 6.1.0 by @dependabot[bot] in #1667
• Bump docker/login-action from 4.0.0 to 4.2.0 by @dependabot[bot] in #1668
• Bump github.com/gorilla/csrf from 1.7.2 to 1.7.3 by @dependabot[bot] in #1361
• Bump golang.org/x/net from 0.55.0 to 0.56.0 by @dependabot[bot] in #1693
• Bump docker/login-action from 4.2.0 to 4.3.0 by @dependabot[bot] in #1695

Upgrading from v0.16.x or earlier

1. Download the latest release for your operating system and architecture
2. Stop running your writefreely server
3. Replace all files in your installation (except for the keys directory) with the ones in the archive
4. Update your database by running: writefreely db migrate
5. Start your writefreely server again

If you're upgrading from a much earlier version, follow the instructions in each previous release.

New Contributors

Thank you to all who contributed to this release!

• @smazmi made their first contribution in #1565
• @blacklight made their first contribution in #1437
• @lolbinarycat made their first contribution in #1142
• @robertsilen made their first contribution in #1665

#WriteFreely #release