A popular gay social networking application has said that it is issuing a warning to its users in Egypt, as police impersonate community members to target LGBTQ individuals. Users in Egypt will see the following warning appear in Arabic and English when they open the app: "We have been alerted that Egyptian police is actively making arrests of gay, bi, and trans people on digital platforms. They are using fake accounts and have also taken over accounts from real community members who have already been arrested and had their phones taken. Please take extra caution online and offline, including with accounts that may have seemed legitimate in the past." Egypt, though it technically does not outlaw homosexuality, frequently prosecutes members of the LGBTQ community on the grounds of "debauchery" or "violating public decency".

Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning attacks and their defenses, i.e., new defenses are designed to defend against existing attacks and new attacks are designed to break them. To prevent such a cat-and-mouse game, we propose PORE, the first framework to build provably robust recommender systems in this work. PORE can transform any existing recommender system to be provably robust against any untargeted data poisoning attacks, which aim to reduce the overall performance of a recommender system. Suppose PORE recommends top-$N$ items to a user when there is no attack. We prove that PORE still recommends at least $r$ of the $N$ items to the user under any data poisoning attack, where $r$ is a function of the number of fake users in the attack. Moreover, we design an efficient algorithm to compute $r$ for each user. We empirically evaluate PORE on popular benchmark datasets.

We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user's actions, and thus the rewards. Clustering similar users can improve the quality of reward estimation, which in turn leads to more effective content recommendation and targeted advertising. Different from traditional clustering settings, we cluster users based on the unknown bandit parameters, which will be estimated incrementally. In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. And, we provide theoretical analysis about LOCB in terms of the correctness and efficiency of clustering and its regret bound. Finally, we evaluate the proposed algorithm from various aspects, which outperforms state-of-the-art baselines.

We consider a setting with $N$ heterogeneous units and $p$ interventions. Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i.e., $N \times 2^p$ causal parameters. Choosing combinations of interventions is a problem that naturally arises in many applications such as factorial design experiments, recommendation engines (e.g., showing a set of movies that maximizes engagement for users), combination therapies in medicine, selecting important features for ML models, etc. Running $N \times 2^p$ experiments to estimate the various parameters is infeasible as $N$ and $p$ grow. Further, with observational data there is likely confounding, i.e., whether or not a unit is seen under a combination is correlated with its potential outcome under that combination. To address these challenges, we propose a novel model that imposes latent structure across both units and combinations. We assume latent similarity across units (i.e., the potential outcomes matrix is rank $r$) and regularity in how combinations interact (i.e., the coefficients in the Fourier expansion of the potential outcomes is $s$ sparse). We establish identification for all causal parameters despite unobserved confounding. We propose an estimation procedure, Synthetic Combinations, and establish finite-sample consistency under precise conditions on the observation pattern. Our results imply Synthetic Combinations consistently estimates unit-specific potential outcomes given $\text{poly}(r) \times (N + s^2p)$ observations. In comparison, previous methods that do not exploit structure across both units and combinations have sample complexity scaling as $\min(N \times s^2p, \ \ r \times (N + 2^p))$. We use Synthetic Combinations to propose a data-efficient experimental design mechanism for combinatorial causal inference. We corroborate our theoretical findings with numerical simulations.

As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential to improve the everyday life of the elderly and chronically ill people. This includes, for example, medication reminders, routine checks, or social chit-chat. In addition, conversational agents can satisfy the fundamental need of having access to information about daily news or local events, which enables individuals to stay informed and connected with the world around them. However, finding relevant news sources and navigating the plethora of news articles available online can be overwhelming, particularly for those who may have limited technological literacy or health-related impairments. To address this challenge, we propose an innovative solution that combines knowledge graphs and conversational agents for news search in assisted living. By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people to easily discover relevant news articles and give personalized recommendations. We explain our design choices, provide a system architecture, share insights of an initial user test, and give an outlook on planned future work.

Artificial intelligence (AI) is becoming increasingly important in our daily lives. AI can automate routine and time-consuming tasks, allowing us to focus on more important activities. In addition, AI algorithms can analyze vast amounts of data to personalize products, services and experiences. Moreover, AI is driving innovation in various industries, such as finance, retail and education. Here are seven artificial intelligence examples in everyday life.

Artificial intelligence (AI) is becoming increasingly important in our daily lives. AI can automate routine and time-consuming tasks, allowing us to focus on more important activities. In addition, AI algorithms can analyze vast amounts of data to personalize products, services and experiences. Moreover, AI is driving innovation in various industries, such as finance, retail and education. Here are seven artificial intelligence examples in everyday life.

Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to their type (e.g., bug, question, good first issue, feature, etc.). In contrast, this paper presents a tool (GiveMeLabeledIssues) that mines project repositories and labels issues based on the skills required to solve them. We leverage the domain of the APIs involved in the solution (e.g., User Interface (UI), Test, Databases (DB), etc.) as a proxy for the required skills. GiveMeLabeledIssues facilitates matching developers' skills to tasks, reducing the burden on project maintainers. The tool obtained a precision of 83.9% when predicting the API domains involved in the issues. The replication package contains instructions on executing the tool and including new projects. A demo video is available at https://www.youtube.com/watch?v=ic2quUue7i8

The field of user experience (UX) based on the design philosophy of "user-centered design" is moving towards the intelligence era. Still, the existing UX paradigm mainly aims at non-intelligent systems and lacks a systematic approach to UX for intelligent systems. Throughout the development of UX, the UX paradigm shows the evolution characteristics of the cross-technology era. At present, the intelligence era has put forward new demands on the UX paradigm. For this reason, this paper proposes a "UX 3.0" paradigm framework and the corresponding UX methodology system in the intelligence era. The "UX 3.0" paradigm framework includes five categories of UX methods: ecological experience, innovation-enabled experience, AI-enabled experience, human-AI interaction-based experience, and human-AI collaboration-based experience methods, each providing corresponding multiple UX paradigmatic orientations. The proposal of the "UX 3.0" paradigm helps improve the existing UX methods and provides methodological support for the research and applications of UX in developing intelligent systems. Finally, this paper looks forward to future research and applications of the "UX 3.0" paradigm.

The following post is an excerpt from my book'Designing Human-Centric AI Experiences' on applied UX design for Artificial intelligence. We tend to anthropomorphize AI systems, i.e., we impute them with human-like qualities. Many popular depictions of AI, like Samantha in the movie Her or Ava in Ex-Machina, show a personality and sometimes even display emotions. Many AI systems like Alexa or Siri are designed with a personality in mind. However, choosing to give your AI system a personality has its advantages and disadvantages.