Source: National Bureau of Economic Research

2 links

www.nber.org > Alan L. Zhang, Baozhong Yang, Sean Cao and Wei Jiang
How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI
1 oct. 2020 - This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.
 · artificial-intelligence · corporate-disclosure · not-read · transparency

www.nber.org > Marianne Bertrand and Sendhil Mullainathan
Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination
1 jul. 2003 - We perform a field experiment to measure racial discrimination in the labor market. We respond with fictitious resumes to help-wanted ads in Boston and Chicago newspapers. To manipulate perception of race, each resume is assigned either a very African American sounding name or a very White sounding name. The results show significant discrimination against African-American names: White names receive 50 percent more callbacks for interviews. We also find that race affects the benefits of a better resume. For White names, a higher quality resume elicits 30 percent more callbacks whereas for African Americans, it elicits a far smaller increase. Applicants living in better neighborhoods receive more callbacks but, interestingly, this effect does not differ by race. The amount of discrimination is uniform across occupations and industries. Federal contractors and employers who list Equal Opportunity Employer' in their ad discriminate as much as other employers. We find little evidence that our results are driven by employers inferring something other than race, such as social class, from the names. These results suggest that racial discrimination is still a prominent feature of the labor market.
 · black-struggle · recruitment · united-states