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24 An Intelligence in Our Image ulate concerns that the algorithmic SketchFactor score would likely encode preexisting cultural biases about neighborhoods. Combining algorithmic literacy with transparency could be very effective. Transparency in this space usually refers to making sure any algorithms in use are easily understood. Again, that is unlikely to be feasible all the time. What is feasible and useful is more disclosure of decisions and actions that are mediated by artificial agents. Users seem to already expect such disclosures as the backlash over Facebook’s new curation practices indicates (Tufekci, 2016). Beyond just satisfying user tastes, disclosure-style transparency allows users to be better informed and more skeptical consumers of information. Personnel Approaches The technical research on bias in machine learning and artificial intel- ligence algorithms is still in its infancy. Questions of bias and systemic errors in algorithms demand a different kind of wisdom from algorithm designers and data scientists. These practitioners are often engineers and scientists with less exposure to social or public policy questions. The demographics of algorithm designers are often less than diverse. Algorithm designers make myriad design choices, some of which may have far-reaching consequences. Diversity in the ranks of algorithm developers could help improve sensitivity to potential disparate impact problems. However the drive to remedy algorithmic bias should be tem- pered with a healthy dose of regulatory restraint. Any kind of remedy would require algorithms to adhere more closely to socially defined values. Which values, and who gets to decide? Questions about free speech, censorship, equitability, and other acceptable ethical standards will need to be addressed as society wades deeper into these waters.PDF Image | Intelligence in Our Image Risks of Bias and Errors in Artificial Intelligence
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