A major focus of Computer Weekly's technology and ethics coverage in 2022 was on working conditions throughout the tech sector, from the issue of forced labour and slavery throughout technology supply chains, to UK Amazon workers staging spontaneous "wildcat" strikes in response to derisory pay rises and warehouse conditions. Other stories in this vein included coverage of accusations that "soft union-busting" tactics were used by app-based food delivery firm Deliveroo to scupper its workers' grassroots organising efforts, and the ongoing court case against five major tech firms for their alleged role in the maiming and deaths of people extracting raw materials in the Democratic Republic of Congo. Artificial intelligence (AI) also featured heavily in Computer Weekly's technology and ethics coverage in 2022, with stories published on the tech sector's lacklustre commitment to "ethical" AI, as well as on the pitfalls and challenges of auditing AI-powered algorithms. Police technology was another major focus of 2022, as policing bodies continue to push ahead with new tech deployments such as live facial recognition (LFR) despite serious concerns about its effectiveness, proportionality and efficacy. Other stories focused on how technology is developed and deployed, and the underlying power dynamics at play.

The defendant will be represented by artificial intelligence in court for the first time. DoNotPay leverages AI to help defendants against parking tickets and fines and create legal documents. Users can sign up for DoNotPay on an annual subscription for $36 per year. History is said to be made in February as an artificial intelligence bot will advise the defendant for the first time in a court hearing. As per a report, the world's first robot lawyer will run on the defendant's smartphone through an app called'DoNotPay' and listen to court arguments in real time, telling the defendant what to say via earpiece.

An artificial intelligence developed by DoNotPay is expected to advise a defendant in court this February in possibly the first-ever case argued by an AI, Metro reported on Friday. The AI will provide legal advice to a defendant on trial for a speeding ticket via an earpiece, according to the New Scientist. DoNotPay CEO Joshua Browder pledged to recompensate the defendant for any fines that could be incurred if the case is lost. Browder initially launched the company in 2015 as a chatbot that provides legal advice to people facing fines or late fees, according to the Metro report. Browder said that there are liability risks and that he is training the AI on case law and making sure it remains honest, according to NDTV.

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clustering, associates a color with each point representing its group membership and requires that each color has (approximately) equal representation in each cluster to satisfy group fairness. In this model, the cost of the clustering objective increases due to enforcing fairness in the algorithm. The relative increase in the cost, the ''price of fairness,'' can indeed be unbounded. Therefore, in this paper we propose to treat an upper bound on the clustering objective as a constraint on the clustering problem, and to maximize equality of representation subject to it. We consider two fairness objectives: the group utilitarian objective and the group egalitarian objective, as well as the group leximin objective which generalizes the group egalitarian objective. We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them. For the leximin objective we introduce an effective heuristic algorithm. We further derive impossibility results for other natural fairness objectives. We conclude with experimental results on real-world datasets that demonstrate the validity of our algorithms.

Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the major tools for inferring network topology is community detection. Although there are numerous works on community detection in single-layer networks, existing community detection methods for multiplex networks mostly learn a common community structure across layers and do not take the heterogeneity across layers into account. In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer. The proposed method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, represents the adjacency matrix of each layer as the sum of two low-rank matrix factorizations corresponding to the common and private communities, respectively. Unlike most of the existing methods, which require the number of communities to be pre-determined, the proposed method also introduces a two stage method to determine the number of common and private communities. The proposed algorithm is evaluated on synthetic and real multiplex networks, as well as for multiview clustering applications, and compared to state-of-the-art techniques.

An artificial intelligence model from DoNotPay will officially act as legal counsel for a defendant in a real-life court case. What if artificial intelligence could argue a court case for defendants? A San Francisco-based company called DoNotPay is poised to answer that question as it has developed an AI that will be advising the recipient of a speeding ticket in a case that will be heard in February. Futurism reports that the artificial legal assistant will be communicating with the defendant via earpiece. This will be the first time that artificial intelligence is used in this capacity as it is illegal to use technology this way in many places, but DoNotPay found a pocket where the assistant can be implemented.

Historically humans had various prejudices and biases like racism, classism, antisemitism, ableism, sexism, misogyny, etc. No matter which human society you live in, it would have been peppered with prejudices based on sex, gender, religion, complexion, beauty, social class, etc. in the past. However, now, we know better. The extent to which human bias can infiltrate artificial intelligence (AI) systems and cause detrimental damage is a hot topic in the tech community. To put it simply, AI bias is a problem that appears when an AI algorithm generates results that are systematically skewed due to false assumptions made during the AI training process.

If you are an attorney interested in attending this program, please click the link below to email your name, title, company, contact information, and bar numbers for jurisdictions in which you are requesting CLE credit to DechertCLE@dechert.com. Once we receive this information, we will confirm your registration status. This session will be run via Webex. Please see here for their minimum system requirements. Artificial intelligence, new software, and robotic process automation continue to change how diligence is conducted at law firms.

In the past few years, AI has burst onto the scene like never before, writing poetry, computer code, and even college essays at the drop of a hat. With every new iteration of the programs being released, the capabilities of the bots have been rising, and of late, AI has entered the art scene as well put forward its most creative side. In a new development, a company, DoNotPay, which has been training AI, has now claimed that its program will be able to defend a speeding case that is due to be heard in a U.S. court in February 2023. Identities of the individual and the court remain under wraps, but we do know that the defendant is contesting a speeding ticket. Founded in 2015, DoNotPay is an AI solution that is aimed at helping individuals fight against large organizations for acts such as applying wrong fees, persistent robocalling, or even fighting parking tickets.

A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving system will satisfy perfect secrecy, meaning that interactions with the system provably reveal no private information. However, privacy and quality appear to be in tension in existing systems for personal tasks. Neural models typically require copious amounts of training to perform well, while individual users typically hold a limited scale of data, so federated learning (FL) systems propose to learn from the aggregate data of multiple users. FL does not provide perfect secrecy, but rather practitioners apply statistical notions of privacy -- i.e., the probability of learning private information about a user should be reasonably low. The strength of the privacy guarantee is governed by privacy parameters. Numerous privacy attacks have been demonstrated on FL systems and it can be challenging to reason about the appropriate privacy parameters for a privacy-sensitive use case. Therefore our work proposes a simple baseline for FL, which both provides the stronger perfect secrecy guarantee and does not require setting any privacy parameters. We initiate the study of when and where an emerging tool in ML -- the in-context learning abilities of recent pretrained models -- can be an effective baseline alongside FL. We find in-context learning is competitive with strong FL baselines on 6 of 7 popular benchmarks from the privacy literature and a real-world case study, which is disjoint from the pretraining data. We release our code here: https://github.com/simran-arora/focus