4 résultats pour « bias »
The research shows that AI biases often stem from organizational pressures like cost, risk, competition, and compliance, influencing development before technical factors are considered. These biases reflect broader societal and commercial contexts, with ethical considerations often sidelined. Recommendations focus on assessing technology's impact and organizational influences on AI biases.
The article delves into #ethical concerns with #aitools in #legal and #tax research, addressing #output #quality, #bias, #verifiability, #liability, and #privacy #risks. It explores #regulatory, #tech, and professional solutions, offering practical advice for tax professionals to safely navigate AI's challenges with #riskmitigation.
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"By comparing the decisions output by diverse settings, we find that ML algorithms can mitigate both the preference-based bias and the belief-based bias, while the effects vary for new and repeated applicants. Based on our findings, we propose a two-step human-AI collaboration framework for practitioners to reduce decision bias most effectively."