Record summary
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Risk profile
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Financial harms [52, 160] co-produced through algorithmic systems, especially as they relate to lived experiences of poverty and economic inequality... demonetization algorithms that parse content titles, metadata, and text, and it may penalize words with multiple meanings [51, 81], disproportionately impacting queer, trans, and creators of color [81]. Differential pricing algorithms, where people are systematically shown different prices for the same products, also leads to economic loss [55]. These algorithms may be especially sensitive to feedback loops from existing inequities related to education level, income, and race, as these inequalities are likely reflected in the criteria algorithms use to make decisions [22, 163].
Suggested mitigations
Defenses that may help with related attacks.
Source
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Included resource
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.