Bostock resolved the issue through analysis of three cases:R.G. & G.R. Harris Funeral Homes Inc. v. Equal Employment Opportunity Commission, where Aimee Stephens worked as a funeral director at R.G. & G.R. Harris Funeral Homes. When Aimee informed the funeral home’s owner that she was transgender, the business owner fired her, saying it would be “unacceptable” for her to appear and behave as a woman. Altitude Express v. Zarda, where Donald Zarda, a skydiving instructor in Long Island, N.J., was fired from his job because of his sexual orientation. Bostock v. Clayton County, where Gerald Lynn Bostock was fired from his job as a county child welfare services coordinator when his employer learned Gerald is gay. Helpful Resources // “Big Data” and the Risk of Employment Discrimination, by Allan King & Marko Mrkonich, grappling with the many ways employers use correlative methods of analyzing big data, and how those efforts are in tension with Title VII, the to the extent the correlations those methods discover overlap with protected employee characteristics.Big Data for All: Privacy and User Control in the Age of Analytics, by Omer Tene and Jules Polonetsky, noting that inaccurate, manipulative, or discriminatory conclusions may be drawn from perfectly innocuous, accurate data—and like any interpretative process, algorithms are subject to error, inaccuracy, and bias.The Real Cost of LGBT Discrimination, by the World Economic Forum, recognizing that discrimination doesn’t just harm individuals—but also families, companies, and entire countries. Challenges for Mitigating Bias in Algorithmic Hiring, by The Brookings Institution, recognizing that, in instances of disparate impact caused by automated decision making, there are numerous bars to relief, including the fact that (1) plaintiffs may not have sufficient information to suspect or demonstrate disparate impact; (2) it is unclear whether predictive validity is sufficient to defend against a claim of disparate impact;and (3) many proposed solutions to mitigating disparities from screening decisions require knowledge of legally protected characteristics.Lessons from Fair Lending Law for Fair Marketing and Big Data, by Peter Swire, explaining that fair lending laws provide guidance as to how to approach discrimination that allegedly has an illegitimate, disparate impact on protected classes; furthermore data can plays an important role in being able to assess whether a disparate impact exists. Algorithmic Fairness, by the Software & Information Industry Association, explaining that automated decision making can be designed and used in ways that preserve fairness for all, but this will not happen automatically–getting those outcomes requires designing those features and using them in ways that preserve these values. Todays 6-3 ruling aligns with Obama-era protections, including a 2014 executive order extending Title VII protections to LGBTQ individuals working for the federal contractors.