概要: AI writing detection tools have reportedly continued to falsely flag genuine student work as AI-generated, disproportionately impacting ESL and neurodivergent students. Specific cases include Moira Olmsted, Ken Sahib, and Marley Stevens, who were penalized despite writing their work independently. Such tools reportedly exhibit biases, leading to academic penalties, probation, and strained teacher-student relationships.
Editor Notes: Reconstructing the timeline of events: (1) Sometime in 2023: Central Methodist University is reported to have used Turnitin to analyze assignments for AI usage. Moira Olmsted’s writing is flagged as AI-generated, leading to her receiving a zero and a warning. (2) Sometime in 2023: Ken Sahib, an ESL student at Berkeley College, is reported to have been penalized after AI detection tools flagged his assignment as AI-generated. (3) Sometime in late 2023 or early 2024: Marley Stevens is reported to have been placed on academic probation after Turnitin falsely identifies her work as AI-generated, though she purports to have only used Grammarly for minor edits. (4) October 18, 2024: Bloomberg publishes findings that leading AI detectors falsely flag 1%-2% of essays as AI-generated, with higher error rates for ESL students. (This date is set as the incident date for convenience.)
Alleged: Turnitin , GPTZero と Copyleaks developed an AI system deployed by Central Methodist University , Berkeley College , Universities と Colleges, which harmed students , Neurodivergent students , ESL students , Moira Olmsted , Ken Sahib と Marley Stevens.
インシデントのステータス
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.3. Lack of capability or robustness
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- AI system safety, failures, and limitations
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
インシデントレポート
レポートタイムライン
パンデミック初期に家庭を持つために大学を休学した後、モイラ・オルムステッドさんは 学校に戻ることを熱望していた。何カ月もの間、フルタイムの仕事と幼児の育児を両立させ、リモート学習を可能にする自分のペースで学べるプログラムのためにお金を貯めた。2人目の子供を妊娠7カ月のオルムステッドさんは、2023年にセントラル・メソジスト大学のオンラインコースに登録し、教師になるための勉強をした。
秋学期が始まってわずか数週間で、オルムステッドさんは必修科目の課題を提出した。毎週やらなければな…
バリアント
「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要 求されません。詳細についてはこの研究論文を参照してください
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