Human–AI Interaction

How people perceive, trust, and collaborate with AI systems. This sub-article expands Section 2.2 of Philosophy.

Trust and Calibration

• People over- or under-trust algorithmic advice.
• Calibration requires transparency, feedback, and appropriate reliance.

Anthropomorphism

• Users attribute intent, emotion, and personality to chatbots (the ELIZA effect).
• This can increase engagement but also create unrealistic expectations.

Bias and Fairness

• Models reflect and amplify human biases in hiring, lending, policing.
• Fairness research studies disparate impact and mitigation.

Overreliance and Errors

• Users may defer to AI even when it errs (“automation bias”).
• Design must support questioning and verification.

Why It Matters

Interaction design determines whether AI helps or harms, and how people understand machine behavior.

Sources

• Weizenbaum, E. (1966). ELIZA.
• Bansal, G. et al. (2021). Does the Whole Exceed its Parts? (human-AI teams).

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