Right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The General Data Protection Regulation in the European Union legally solidified the status of this long held notion. As a consequence, there is a growing need for the development of mechanisms whereby users can verify if service providers comply with their deletion requests. In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- in the context of systems that provide machine learning as a service. We propose a backdoor-based verification mechanism and demonstrate its effectiveness in certifying data deletion with high confidence using the above framework. Our mechanism makes a novel use of backdoor attacks in ML as a basis for quantitatively inferring machine unlearning. In our mechanism, each user poisons part of its training data by injecting a user-specific backdoor trigger associated with a user-specific target label. The prediction of target labels on test samples with the backdoor trigger is then used as an indication of the user's data being used to train the ML model. We formalize the verification process as a hypothesis testing problem, and provide theoretical guarantees on the statistical power of the hypothesis test. We experimentally demonstrate that our approach has minimal effect on the machine learning service but provides high confidence verification of unlearning. We show that with a $30\%$ poison ratio and merely $20$ test queries, our verification mechanism has both false positive and false negative ratios below $10^{-5}$. Furthermore, we also show the effectiveness of our approach by testing it against an adaptive adversary that uses a state-of-the-art backdoor defense method.

Will we grant human rights to AI, and if so, what are the potential consequences. Should AI have human rights? It's a seemingly simple question, though the answer has tremendous consequences. Presumably, your answer is either that yes, AI should have human rights, or alternatively, that AI should not have human rights. There is a bit of a trick involved though because the thing or entity or "being" that we are trying to assign human rights to is currently ambiguous and currently not even yet in existence.

Considering the vast amount of data present, various insights into the data have led to data science professionals to get information about human behaviour, and that's where the question of ethics arises. As data science's focal point is analysing the data generated by humans, how data scientists use it should come under certain ethical conditions. Today's era offers a great deal of ease when it comes to analytics, which completely changes the ethical framework. The scalability of the data also raises several ethical issues, especially with the companies who use data for monetising it. With the existing ethical frameworks, it is not clear about what to follow, but some basic principles should always be addressed when looking at data science ethics: With advancements like robotics and automation, the gap between humans and artificial intelligence is closing.

John Catsimatidis recently used artificial intelligence to do a background search on his daughter's date, the billionaire founder of New York grocery store giant Gristedes told the New York Times on Friday. Catsimatidis was having dinner at an Italian restaurant in Manhattan when he saw his daughter walk in with a man he didn't recognize, so he asked his waiter to take a photo of the man's face, and then he uploaded that photo to his Clearview AI facial recognition app. "I wanted to make sure he wasn't a charlatan," he told the Times. Clearview AI uses artificial intelligence technology to connect photos from its enormous collection pulled from Google, Facebook, Instagram and other social media to specific individuals. It was originally intended for law-enforcement use, but individuals have also been reported using the app as a result of free business trials.

In addition to random celebrity sightings, residents of West Hollywood, CA will soon be spotting autonomous delivery robots in their neighborhood. Last night the West Hollywood city council approved the use of delivery robots on its city streets (hat tip to WeHoVille). A trial of the program will start next month with Postmates' Serve robot and run for 90 days. Serve is a cooler-sized robot that scurries around on four wheels, and while it can run autonomously using sensors and cameras to avoid people and obstacles, the city council is requiring a human chaperone during the trial. Additionally, only three robots can be in operation at once, they can only run during the day, and they aren't allowed on sidewalks deemed substandard.

The use of artificial intelligence is growing in many areas: hiring, healthcare, travel, household functions. These AI examples rely heavily on deep learning and natural language processing, but its use has sparked a debate about bias and fairness. Human decision making can be shaped by unconscious individual and societal biases. Will AI's decisions be less biased than human ones? How are companies addressing this potential AI bias with regards to hiring, data crunching, and other critical business functions?

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

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When it comes to contracts, every business learns to deal with several important contractual facts of life. One is that contracts need to accurately protect the company's business interests while adhering to acceptable legal practices and regulatory requirements in relevant jurisdictions. Another is that contracts must be faithfully administered in order to serve their basic business functions. Finally, contract management costs a lot. It is the cost of creating and administering contracts inefficiently.

Phoenix Air Unmanned, LLC (PAU) is seeking information on the availability of Unmanned Aircraft Systems to support linear infrastructure inspections. The UAS will be operated by PAU who has been contracted by Xcel Energy as their unmanned flight service provider and they plan to purchase a minimum of 4 aircraft initially with the possibility of additional aircraft in the future. Xcel Energy, Inc. owns over 120,000 miles of transmission and distribution infrastructure across eight states (CO, MI, MN, NM, WI, ND, SD, TX) that must be inspected at regular intervals as required by state and federal regulations. Xcel Energy, Inc. is a utility holding company with a service company (Xcel Energy Services) and four wholly owned utility subsidiaries that serve electric and natural gas customers. PAU was established in 2014 for commercial UAS operations.