#AIBias is an active hashtag on Bluesky. In the last 30 days, 18 people shared 29 posts with it — around 1 a day. Activity is up 33% versus the previous week, peaking on Jul 8 with 3 posts.
Tags most often used together with #AIBias.
doi.org
Assisting or resisting patriarchy? a critical discourse analysis of chatgpt’s responses on feminism - AI and Ethics
This study examined ChatGPT outputs about feminism. Previous research on commercial products built on large language model (LLM) architecture has demonstrated the presence of biases, introducing a danger of reproducing societal harm at scale through mis/disinformation, despite safeguards against malicious activity. We sought to understand these safeguards through analysis of how a commercial product, ChatGPT, assists or resists the generation of feminist or anti-feminist outputs under different prompting conditions. Drawing on software studies, we applied multiple approaches to critical discourse analysis (cDA) to analyze ChatGPT outputs. GPT-4 was prompted to generate outputs about feminism while assuming the roles of individuals and literary characters with a range of feminist, sexist, or misogynistic views. We analyzed these outputs and interactions by focusing on the linguistic elements of the outputs as well as the resistance or assistance in (re)producing anti-feminist discourse against the stated ethical guidelines of the product. The findings demonstrated that in a feminist role, the model produced themes consistent with third wave feminism while in sexist or misogynistic roles, the model reproduced patriarchal discourses. When prompted to produce anti-feminist outputs, the model displayed limited resistance; however, with repeated and revised prompting, the model quickly normalized violations of its own safeguards. While feminist and anti-feminist discourses in this LLM-based commercial product were both present, this study demonstrated anti-feminist outputs are readily accessible given the product’s prioritization of user desires. The study highlights the need for stronger safeguards and greater developer accountability to prevent harmful responses and malicious use of generative AI.
www.nature.com
Digital divides in scene recognition: uncovering socioeconomic biases in deep learning systems - Humanities and Social Sciences Communications
Humanities and Social Sciences Communications - Digital divides in scene recognition: uncovering socioeconomic biases in deep learning systems
www.citizenofeurope.com
Coded Male: The AI Systems That Don’t See Women - Citizen Of Europe
AI gender bias systems quietly exclude women — from healthcare to hiring. Citizen of Europe investigates how neutrality became the new discrimination.
www.theguardian.com
‘Orwellian’: Sainsbury’s staff using facial recognition tech eject innocent shopper
Man misidentified by London supermarket using Facewatch system says: ‘I shouldn’t have to prove I am not a criminal’
www.citizenofeurope.com
Coded Male: The AI Systems That Don’t See Women - Citizen Of Europe
AI gender bias systems quietly exclude women — from healthcare to hiring. Citizen of Europe investigates how neutrality became the new discrimination.
blogs.lse.ac.uk
The Matthew effect in AI summary - LSE Impact
AI research tools are trained on a literature that is structured by an unequal distribution of attention. Does their use simply recreate these biases ?
www.medianama.com
Gujarat HC AI Policy Bans Use in Court Decisions & Orders
Gujarat High Court sets strict AI policy, allowing limited use while banning AI in judicial decisions, citing risks of bias and over-reliance.
citizenportal.ai
Speakers point to intentional or emergent bias in some AI services
The webinar covered cases where platform choices and focused data streams produced biased outcomes—Gadeau cited Grok (Twitter/X) and DeepSeek as documented examples where source selection or likely intent shaped unfair outputs.
doi.org
Assisting or resisting patriarchy? a critical discourse analysis of chatgpt’s responses on feminism - AI and Ethics
This study examined ChatGPT outputs about feminism. Previous research on commercial products built on large language model (LLM) architecture has demonstrated the presence of biases, introducing a danger of reproducing societal harm at scale through mis/disinformation, despite safeguards against malicious activity. We sought to understand these safeguards through analysis of how a commercial product, ChatGPT, assists or resists the generation of feminist or anti-feminist outputs under different prompting conditions. Drawing on software studies, we applied multiple approaches to critical discourse analysis (cDA) to analyze ChatGPT outputs. GPT-4 was prompted to generate outputs about feminism while assuming the roles of individuals and literary characters with a range of feminist, sexist, or misogynistic views. We analyzed these outputs and interactions by focusing on the linguistic elements of the outputs as well as the resistance or assistance in (re)producing anti-feminist discourse against the stated ethical guidelines of the product. The findings demonstrated that in a feminist role, the model produced themes consistent with third wave feminism while in sexist or misogynistic roles, the model reproduced patriarchal discourses. When prompted to produce anti-feminist outputs, the model displayed limited resistance; however, with repeated and revised prompting, the model quickly normalized violations of its own safeguards. While feminist and anti-feminist discourses in this LLM-based commercial product were both present, this study demonstrated anti-feminist outputs are readily accessible given the product’s prioritization of user desires. The study highlights the need for stronger safeguards and greater developer accountability to prevent harmful responses and malicious use of generative AI.
synapseglass.wordpress.com
AI chatbots reveal hidden political bias in mirror of data
AI chatbots reveal hidden political bias in mirror of data
wellfunded.news
AI Hiring Tools Amplify Bias, and Weather Data Faces a New Sabotage Threat
New research shows LLMs can develop their own biases when screening job applicants — potentially outpacing human prejudice. Meanwhile, the rise of AI-driven weather forecasting and prediction markets is creating dangerous new incentives to manipulate meteorological data.
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