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  2. Algorithmic bias - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_bias

    This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas.

  3. Ethics of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Ethics_of_artificial...

    The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes. [1] This includes algorithmic biases, fairness, automated decision-making, accountability, privacy, and regulation.

  4. ChatGPT’s creators say AI has been ‘biased, offensive and ...

    www.aol.com/chatgpt-creators-ai-biased-offensive...

    ChatGPT creators OpenAI say the system has been “politically biased, offensive” and “otherwise objectionable”, and has committed to changing how it works.

  5. AI with hidden biases may be subtly shaping what you think ...

    www.aol.com/finance/ai-hidden-biases-may-subtly...

    The only way to combat this kind of hidden bias will be to mandate that tech companies reveal far more about how their AI models have been trained and allow independent auditing and testing.

  6. Representational harm - Wikipedia

    en.wikipedia.org/wiki/Representational_harm

    Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space , which allows an individual to calculate the relationships and similarities between ...

  7. Should we be worried about AI becoming sentient? - AOL

    www.aol.com/news/worried-ai-becoming-sentient...

    Artificial intelligence is being used right now for an increasing number of tasks once carried out by humans — from parole decisions, to facial recognition, to self-driving cars to education.

  8. Fairness (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Fairness_(machine_learning)

    Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).

  9. Teens are using AI, but are worried about what it means for ...

    www.aol.com/finance/teens-using-ai-worried-means...

    Yet, the warning about AI being the first place teens may go hits hard in light of the death of Sewell Setzer III, a 14-year-old from Florida who killed himself after becoming increasingly ...

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