Inferring Capabilities of Intelligent Agents

We investigate the usability of human-like agent-based interfaces. In an experiment we manipulate the capabili­ties and the “human-likeness” of a travel advisory agent. We show that users of the more human-like agent form an anthropomorphic use image of the system: they act as if the system is human, and try to exploit typical human-like capabilities. Unfortu­nately, this severely reduces the usa­bility of the agent that looks human but lacks human-like capabilities (overestima­tion effect). We also show that the use image users form of agent-based systems is inherently integrated (as opposed to the compositional use image they form of conventional GUIs): cues provided by the system do not instill user responses in a one-to-one manner, but are instead integrated into a single use image. Consequently, users try to exploit capabilities that were not signaled by the system to begin with, thereby further exacerbating the overestimation effect

Behaviorism is Not Enough: Better Recommendations Through Listening to Users

Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say — about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system — and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users’ better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users

Look and You Will Find It:Fairness-Aware Data Collection through Active Learning

Machine learning models are often trained on data sets subject to selection bias. In particular, selection bias can be hard to avoid in scenarios where the proportion of positives is low and labeling is expensive, such as fraud detection. However, when selection bias is related to sensitive characteristics such as gender and race, it can result in an unequal distribution of burdens across sensitive groups, where marginalized groups are misrepresented and disproportionately scrutinized. Moreover, when the predictions of existing systems affect the selection of new labels, a feedback loop can occur in which selection bias is amplified over time. In this work, we explore the effectiveness of active learning approaches to mitigate fairnessrelated harm caused by selection bias. Active learning approaches aim to select the most informative instances from unlabeled data. We hypothesize that this characteristic steers data collection towards underexplored areas of the feature space and away from overexplored areas – including areas affectedby selection bias. Our preliminary simulation results confirm the intuition that active learning can mitigate the negative consequences of selection bias, compared to both the baseline scenario and random sampling.<br/

Using latent features diversification to reduce choice difficulty in recommendation lists

Ail important side effect of using recoinmender systems is a phenomenon called "choice overload"; the negative feeling incurred by the increased difficulty to choose from large sets of high quality recommendations. Choice overload has traditionally been related to the size of the item set, but recent work suggests that the diversity of the item set is an important moderator. Using the latent feanires of a matrix factorization algorithm, we were able to manipulate the diversity of the items, while controlling the overall attractiveness of the list of recommendations. In a user study, participants evaluated personalized item lists (varying in level of diversity) on perceived diversity and attractiveness, and their experienced choice difficulty and tradeoff difficulty. The results suggest that diversifying the recommendations might be an effective way to reduce choice overload, as perceived diversity and attractiveness increase with item set diversity, subsequently resulting in participants experiencing less tradeoff difficulty and choice difficulty.</p

Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21)

Recommender systems were originally developed as interactive intelligent systems that can proactively guide users to items that match their preferences. Despite its origin on the crossroads of HCI and AI, the majority of research on recommender systems gradually focused on objective accuracy criteria paying less and less attention to how users interact with the system as well as the efficacy of interface designs from users’ perspectives. This trend is reversing with the increased volume of research that looks beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the "human side" of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary,we introduce the JointWorkshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’21, review its history, and discuss most important topics considered at the workshop

Towards Reflective AI:Needs, Challenges and Directions for Future Research

Harnessing benefits and preventing harms of AI cannot be solved alone through technological fixes and regulation. It depends on a complex interplay between technology, societal governance, individual behaviour, organizational and societal dynamics. Enabling people to understand AI and the consequences of its use and design is a crucial element for ensuring responsible use of AI.In this report we suggest a new framework for the development and use of AI technologies in a way that harnesses the benefits and prevents the harmful effects of AI. We name it Reflective AI. The notion of Reflective AI that we propose calls for adopting a holistic approach in the research and development of AI to investigate both what people need to learn about AI systems to develop better mental models i.e. an experiential knowledge of AI, to be able to use it safely and responsibly, as well as how this can be done and supported

Nationwide Study to Predict Colonic Ischemia after Abdominal Aortic Aneurysm Repair in The Netherlands

BACKGROUND: Colonic ischemia remains a severe complication after abdominal aortic aneurysm (AAA) repair and is associated with a high mortality. With open repair being one of the main risk factors of colonic ischemia, deciding between endovascular or open aneurysm repair should be based on tailor-made medicine. This study aims to identify high-risk patients of colonic ischemia, a risk that can be taken into account while deciding on AAA treatment strategy.METHODS: A nationwide population-based cohort study of 9,433 patients who underwent an AAA operation between 2014 and 2016 was conducted. Potential risk factors were determined by reviewing prior studies and univariate analysis. With logistic regression analysis, independent predictors of intestinal ischemia were established. These variables were used to form a prediction model.RESULTS: Intestinal ischemia occurred in 267 patients (2.8%). Occurrence of intestinal ischemia was seen significantly more in open repair versus endovascular aneurysm repair (7.6% vs. 0.9%; P &lt; 0.001). This difference remained significant after stratification by urgency of the procedure, in both intact open (4.2% vs. 0.4%; P &lt; 0.001) and ruptured open repair (15.0% vs. 6.2%); P &lt; 0.001). Rupture of the AAA was the most important predictor of developing intestinal ischemia (odds ratio [OR], 5.9, 95% confidence interval [CI] 4.4-8.0), followed by having a suprarenal AAA (OR 3.4; CI 1.1-10.6). Associated procedural factors were open repair (OR 2.8; 95% CI 1.9-4.2), blood loss &gt;1L (OR 3.6; 95% CI 1.7-7.5), and prolonged operating time (OR 2.0; 95% CI 1.4-2.8). Patient characteristics included having peripheral arterial disease (OR 2.4; 95% CI 1.3-4.4), female gender (OR 1.7; 95% CI 1.2-2.4), renal insufficiency (OR 1.7; 1.3-2.2), and pulmonary history (OR 1.6; 95% CI 1.2-2.2). Age &lt;68 years proved to be a protective factor (OR 0.5; 95% CI 0.4-0.8). Associated mortality was higher in patients with intestinal ischemia versus patients without (50.6% vs. 5.1%, P &lt; 0.001). Each predictor was given a score between 1 and 4. Patients with a score of ≥10 proved to be at high risk. A prediction model with an excellent AUC = 0.873 (95% CI 0.855-0.892) could be formed.CONCLUSIONS: One of the main risk factors is open repair. Several other risk factors can contribute to developing colonic ischemia after AAA repair. The proposed prediction model can be used to identify patients at high risk for developing colonic ischemia. With the current trend in AAA repair leaning toward open repair for better long-term results, our prediction model allows a better informed decision can be made in AAA treatment strategy.</p