Irẹjẹ AI

Diẹ ninu awọn ilana le ṣe awọn abajade aiṣedeede tabi ipalara. Igbelewọn irẹjẹ nilo lati ṣalaye ipo ati awọn abajade, kii ṣe yiyọ ọwọn ti o ni itara lati inu data kan.

Awọn gbigba bọtini

• Ṣe iwadi data ati wiwọn aṣayan.
• Ṣe ijabọ awọn abajade ẹgbẹ ti o yẹ pẹlu aidaniloju.
• Ṣe ayẹwo iṣẹ ṣiṣe ti o gbooro sii ati atunṣe.

Examine how examples and labels were collected. Missing populations, inconsistent annotation, historical decisions, and selective measurement can shape what the model learns. A target label may reflect an old process rather than the underlying result people care about. Measure performance across relevant groups and conditions with suitable privacy controls. Report sample sizes and aidout. A small subgroup can have unreliable estimates, while a global average can hide a large and practically important disparity. Different fairness criteria answer different questions and can conflict. Equalizing one statistical measure does not settle every ethical or legal concern. Choose criteria with domain expertise and the participation of people affected by the system. Review the workflow around the model. How predictions are used, who can challenge an result, and how feedback is collected can change the distribution of harm. Evaluate mitigations for both their intended effect and possible new problems. Treat fairness as an ongoing assessment rather than a one-time certificate.

1. Ninu idanwo ti a ṣe, ẹgbẹ A ni awọn apẹẹrẹ 900 pẹlu deede 95%, lakoko ti ẹgbẹ B ni awọn apẹẹrẹ 100 pẹlu deede 60%.
2. Abajade lapapọ jẹ gaba lori nipasẹ ẹgbẹ A. Ṣe ijabọ ẹgbẹ B lọtọ ati ṣayẹwo awọn aṣiṣe rẹ ati aidaniloju ayẹwo.
3. Ṣe iwadii data ati awọn okunfa iṣan-iṣẹ ṣaaju ki o to yan idinku kan.

Awọn iṣiro iṣaro wọnyi ṣe afihan idi ti Dimegilio apapọ ko le fi idi iṣẹ deede mulẹ.