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In 2021, Google announced they would disable third-party cookies in the Chrome browser in order to improve user privacy. They proposed FLoC as an alternative, meant to enable interest-based advertising while mitigating risks of individualized user tracking. The FLoC algorithm assigns users to 'cohorts' that represent groups of users with similar browsing behaviors so that third-parties can serve users ads based on their group. After testing FLoC in a real world trial, Google canceled the proposal, with little explanation, in favor of new alternatives to third-party cookies. In this work, we offer a post-mortem analysis of how FLoC handled balancing utility and privacy. In particular, we analyze two potential problems raised by privacy advocates: FLoC (1) allows individualized user tracking rather than prevents it and (2) risks revealing sensitive user demographic information, presenting a new privacy risk. We test these problems by implementing FLoC and compute cohorts for users in a dataset of browsing histories collected from more than 90,000 U.S. devices over a one-year period. For (1) we investigate the uniqueness of users' cohort ID sequences over time. We find that more than 95% are uniquely identifiable after 4 weeks. We show how these risks increase when cohort IDs are combined with fingerprinting data. While these risks may be mitigated by frequently clearing first-party cookies and increasing cohort sizes, such changes would degrade utility for users and advertisers, respectively. For (2), although we find a statistically significant relationship between domain visits and racial background, we do not find that FLoC risks correlating cohort IDs with race. However, alternative clustering techniques could elevate this risk. Our contributions provide example analyses for those seeking to develop novel approaches to monetizing the web in the future.

The purpose of this study is to examine the long-run relationship between gold prices and Nepal Stock Exchange (NEPSE).

This paper investigates the impact of information and communication technology (ICT) adoption on individual well-being.

Online markets are a part of everyday life, and their rules are governed by algorithms. Assuming participants are inherently self-interested, well designed rules can help to increase social welfare. Many algorithms for online markets are based on prices: the seller is responsible for posting prices while buyers make purchases which are most profitable given the posted prices. To make adjustments to the market the seller is allowed to update prices at certain timepoints. Posted prices are an intuitive way to design a market. Despite the fact that each buyer acts selfishly, the seller's goal is often assumed to be that of social welfare maximization. Berger, Eden and Feldman recently considered the case of a market with only three buyers where each buyer has a fixed number of goods to buy and the profit of a bought bundle of items is the sum of profits of the items in the bundle. For such markets, Berger et. al. showed that the seller can maximize social welfare by dynamically updating posted prices before arrival of each buyer. B\'{e}rczi, B\'{e}rczi-Kov\'{a}cs and Sz\"{o}gi showed that the social welfare can be maximized also when each buyer is ready to buy at most two items. We study the power of posted prices with dynamical updates in more general cases. First, we show that the result of Berger et. al. can be generalized from three to four buyers. Then we show that the result of B\'{e}rczi, B\'{e}rczi-Kov\'{a}cs and Sz\"{o}gi can be generalized to the case when each buyer is ready to buy up to three items. We also show that a dynamic pricing is possible whenever there are at most two allocations maximizing social welfare.

The successful application of machine learning (ML) methods becomes increasingly dependent on their interpretability or explainability. Designing explainable ML systems is instrumental to ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingredient for trustworthy artificial intelligence. A key challenge in ensuring explainability is its dependence on the specific human user ("explainee"). The users of machine learning methods might have vastly different background knowledge about machine learning principles. One user might have a university degree in machine learning or related fields, while another user might have never received formal training in high-school mathematics. This paper applies information-theoretic concepts to develop a novel measure for the subjective explainability of the predictions delivered by a ML method. We construct this measure via the conditional entropy of predictions, given a user signal. This user signal might be obtained from user surveys or biophysical measurements. Our main contribution is the explainable empirical risk minimization (EERM) principle of learning a hypothesis that optimally balances between the subjective explainability and risk. The EERM principle is flexible and can be combined with arbitrary machine learning models. We present several practical implementations of EERM for linear models and decision trees. Numerical experiments demonstrate the application of EERM to detecting the use of inappropriate language on social media.

Digital money is getting a lot of traction recently, a process which may accelerate even more with the advent of Central Bank Digital Currency (CBDC). However, digital money has several disadvantages: Payments are difficult or outright impossible in emergency situations, such as the failure of the electricity grid or internet. CBDC may also be difficult to handle for children, the elderly, or non-resident travelers. To overcome these problems, we design a cash-like CBDC experience in the form of physical money that may ultimately replace physical banknotes and coins. In contrast to classic banknotes and coins, our design is integrated with digital CBDC payment systems. Users can easily access physical money without the involvement of any third parties. We also address user concerns for adopting payment systems from both technical and security perspectives. We introduce a model for trust level, and discuss how our system meets the security concerns of our users.

TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Our work lays the foundation to fill this research gap. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. We provide evidence that all the tested factors influence the content recommended to TikTok users. Further, we identified that the follow-feature has the strongest influence, followed by the like-feature and video view rate. We also discuss the implications of our findings in the context of the formation of filter bubbles on TikTok and the proliferation of problematic content.

Decision support is the science and associated practice that consist in providing recommendations to decision makers facing problems, based on available theoretical knowledge and empirical data. Although this activity is often seen as being concerned with solving mathematical problems and conceiving algorithms, it is essentially an empirical and socially framed activity, where interactions between clients and analysts, and between them and concerned third parties, play a crucial role. Since the 80s, two concepts have structured the literature devoted to analysing this aspect of decision support: validity and legitimacy. Whereas validity is focused on the interactions between the client and the analyst, legitimacy refers to the broader picture: the organisational context, the overall problem situation, the environment, culture, history. Despite its importance, this concept has not received the attention it deserves in the literature in decision support. The present paper aims at filling this gap. For that purpose, we review the literature in other disciplines relevant to elaborate a concept of legitimacy useful in decision support contexts. Based on this review, we propose a general theory of legitimacy, adapted to decision support contexts, encompassing the relevant contributions we found in the literature. According to this general theory, a legitimate decision support intervention is one for which the decision support provider produces a justification that satisfies two conditions: (i) it effectively convinces the decision support provider's interlocutors (effectiveness condition) and (ii) it is organised around the active elicitation of as many and as diverse counterarguments as possible (truthfulness condition). Despite its conceptual simplicity, legitimacy, understood in this sense, is a very exacting requirement, opening ambitious research avenues that we delineate.

We prove two theorems related to the Central Limit Theorem (CLT) for Martin-L\"of Random (MLR) sequences. Martin-L\"of randomness attempts to capture what it means for a sequence of bits to be "truly random". By contrast, CLTs do not make assertions about the behavior of a single random sequence, but only on the distributional behavior of a sequence of random variables. Semantically, we usually interpret CLTs as assertions about the collective behavior of infinitely many sequences. Yet, our intuition is that if a sequence of bits is "truly random", then it should provide a "source of randomness" for which CLT-type results should hold. We tackle this difficulty by using a sampling scheme that generates an infinite number of samples from a single binary sequence. We show that when we apply this scheme to a Martin-L\"of random sequence, the empirical moments and cumulative density functions (CDF) of these samples tend to their corresponding counterparts for the normal distribution. We also prove the well known almost sure central limit theorem (ASCLT), which provides an alternative, albeit less intuitive, answer to this question. Both results are also generalized for Schnorr random sequences.

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.