If the next president favors our bosses' interests over our own, the consequences could be dire for all working people in this country and many others. We know how to fight back against a future Trump administration because we have been there before. What's less clear is whether and to what extent we can count on a Harris administration to be our ally. On stage at the Democratic National Convention, Vice President Harris vowed to center the concerns of working people over those of corporate America. If she stays committed to that path in the face of Silicon Valley's well-funded opposition, she will find dedicated allies in tech workers.

The use of artificial intelligence in the workplace has been at the center of the Equal Employment Opportunity Commission's recent guidance on …

These new technologies, while promising in many respects, have garnered the attention of the Equal Employment Opportunity Commission, which last year launched the Artificial Intelligence and Algorithmic Fairness Initiative. The EEOC announced the initiative's intended mission "to ensure that the use of software, including artificial intelligence (AI), machine learning and other emerging technologies used in hiring and other employment decisions comply with the federal civil rights laws that the EEOC enforces."

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

What might have been a turning point for developers was erased when in 2008 then-Governor Arnold Schwarzenegger signed a new set of overtime exemption rules for computer workers in California, shifting the overtime exemption down from $49.77 an hour or $103,521 a year, to $36 an hour or $75,000 a year. This new rule moved the overtime threshold below the average salaries for many in the games industry -- the average salary for programmers in 2007 was $80,886 and $77,131 for producers -- and it included subtle changes to the language describing the specific kinds of work that could qualify for the exemption to make it more broadly applicable. Labor advocates described the move as a concession to help tech and entertainment companies minimize their overtime liabilities and encourage them to keep jobs in California instead of moving their operations to places with aggressive tax credits and lower cost of doing business, especially Toronto, Montreal, and Vancouver.

Phoebe V. Moore Protecting Workers in the Digital Age: Technology, Outsourcing, and the Growing Precariousness of Work Janine Berg Artificial Intelligence is Watching You at Work: Digital Surveillance, Employee Monitoring, and Regulatory Issues in the EU Context Antonio Aloisi & Elena Gramano What if Your Boss was an Algorithm? Economic Incentives, Legal Challenges, and the Rise of Artificial Intelligence at Work Jeremias Adams-Prassl Privacy 4.0 at Work: Regulating Employment, Technology, and Automation Frank Hendrickx A Seat at the Table: Negotiating Data Processing in the Workplace Ilaria Armaroli & Emanuele Dagnino Job Automation in the 1960s: A Discourse Ahead of its Time (And for Our Time) Miriam A. Cherry

Two Chinese software developers are trying to harness the power of open source software to improve working conditions for coders. Last weekend, Katt Gu and Suji Yan, published the "Anti-996 License," which requires any company that uses the project's software to comply with local labor laws as well as International Labour Organization standards, including the right for workers to collectively bargain and a ban on forced labor. The license is part of the growing Anti-996 Movement in China, which refers to a common schedule of working from 9 am to 9 pm, six days a week. This grueling schedule is allegedly widespread in the Chinese tech startup industry, according to a story in the South China Morning Post last month. Last week, one or more anonymous activists launched a website called 996.ICU, detailing Chinese labor laws that a 996 schedule may violate, including provisions that generally limit work to 44 hours a week and require overtime pay.

Apple is investigating reports that one of its parts suppliers is illegally using high school students on its assembly line. Hong Kong-based human rights group Sacom alleges that Taiwanese manufacturer Quanta Computer has been skirting labor laws by using teenage "interns" to assemble the Apple Watch Series 4. According to the Financial Times, Sacom interviewed nearly 30 high school students working at Quanta Computer's factory in Chongqing, China and found many of them were working on the assembly line. One high schooler said there were about 120 students working in the plant, performing the same procedures over and over again "like a robot." A number of students reported being made to work overnight and night shifts, which is illegal under Chinese labor laws. The high schoolers were supposedly sent to the factories by teachers, and a number of the students said they were told they would not graduate if they didn't complete the internships.

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.