More Like this

1. The Blame Game: Understanding Blame Assignment in Social Media

   https://doi.org/10.1109/TCSS.2023.3261242  

   Xi, Ruijie; Singh, Munindar P. (January 2023, IEEE Transactions on Computational Social Systems)

   Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists. 

   more » « less
2. Who Would Bob Blame? Factors in Blame Attribution in Cyberattacks Among the Non-adopting Population in the Context of 2FA.

   Peck, Sarah; Khan, Mohammad; Fahim, Md; Coman, Emil; Jensen, Theodore; Albayram, Yusuf (January 2020, IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC))

   This study focuses on identifying the factors contributing to a sense of personal responsibility that could improve understanding of insecure cybersecurity behavior and guide research toward more effective messaging targeting non-adopting populations. Towards that, we ran a 2(account type)x2(usage scenario)x2(message type) between-group study with 237 United States adult participants on Amazon MTurk, and investigated how the non-adopting population allocates blame, and under what circumstances they blame the end user among the parties who hold responsibility: the software companies holding data, the attackers exposing data, and others. We find users primarily hold service providers accountable for breaches but they feel the same companies should not enforce stronger security policies on users. Results indicate that people do hold end users accountable for their behavior in the event of a breach, especially when the users’ behavior affects others. Implications of our findings in risk communication is discussed in the paper. 

   more » « less
3. How to evaluate blame

   https://doi.org/doi.org/10.1145/3473573  

   Lazarek, Greenman (January 2021, International Conference on Functional Programming)

   Programming language theoreticians develop blame assignment systems and prove blame theorems for gradually typed programming languages. Practical implementations of gradual typing almost completely ignore the idea of blame assignment. This contrast raises the question whether blame provides any value to the working programmer and poses the challenge of how to evaluate the effectiveness of blame assignment strategies. This paper contributes (1) the first evaluation method for blame assignment strategies and (2) the results from applying it to three different semantics for gradual typing. These results cast doubt on the theoretical effectiveness of blame in gradual typing. In most scenarios, strategies with imprecise blame assignment are as helpful to a rationally acting programmer as strategies with provably correct blame. 

   more » « less
4. Does blame shifting work?

   https://doi.org/10.1145/3371133  

   Lazarek, Lukas; King, Alexis; Sundar, Samanvitha; Findler, Robert_Bruce; Dimoulas, Christos (December 2019, Proceedings of the ACM on Programming Languages)

   Contract systems, especially of the higher-order flavor, go hand in hand with blame. The pragmatic purpose of blame is to narrow down the code that a programmer needs to examine to locate the bug when the contract system discovers a contract violation. Or so the literature on higher-order contracts claims. In reality, however, there is neither empirical nor theoretical evidence that connects blame with the location of bugs. The reputation of blame as a tool for weeding out bugs rests on anecdotes about how programmers use contracts to shift blame and their attention from one part of a program to another until they discover the source of the problem. This paper aims to fill the apparent gap and shed light to the relation between blame and bugs. To that end, we introduce an empirical methodology for investigating whether, for a given contract system, it is possible to translate blame information to the location of bugs in a systematic manner. Our methodology is inspired by how programmers attempt to increase the precision of the contracts of a blamed component in order to shift blame to another component, which becomes the next candidate for containing the bug. In particular, we construct a framework that enables us to ask for a contract system whether (i) the process of blame shifting causes blame to eventually settle to the component that contains the bug; and (ii) every shift moves blame ``closer'' to the faulty component. Our methodology offers a rigorous means for evaluating the pragmatics of contract systems, and we employ it to analyze Racket's contract system. Along the way, we uncover subtle points about the pragmatic meaning of contracts and blame in Racket: (i) the expressiveness of Racket's off-the-shelf contract language is not sufficient to narrow down the blamed portion of the code to the faulty component in all cases; and (ii) contracts that trigger state changes (even unexpectedly, perhaps in the runtime system's data structures or caches) interfere with program evaluation in subtle ways and thus blame shifting can lead programmers on a detour when searching for a bug. These points highlight how evaluations such as ours suggest fixes to language design. 

   more » « less
5. Blame-Free Motion Planning in Hybrid Traffic

   Sanggu Park, Edward Andert (January 2023, IEEE transactions on intelligent vehicles)

   Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety, many studies have shown that traffic accidents in a hybrid traffic environment where both AV and human-driven vehicles (HVs) are present are inevitable because of the unpredictability of HVs. Given that eliminating accidents is impossible, an achievable goal is to design AVs in a way so that they will not be blamed for any accident in which they are involved in. In this paper, we propose BlaFT Rules – or Blame-Free hybrid Traffic motion planning Rules. An AV following BlaFT Rules is designed to be cooperative with HVs as well as other AVs, and will not be blamed for accidents in a structured road environment. We provide proofs that no accidents will happen if all AVs are using a BlaFT Rules conforming motion planner, and that an AV using BlaFT Rules will be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms to BlaFT Rules called BlaFT. We instantiated scores of BlaFT controlled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage of BlaFT vehicles increases, the traffic becomes safer even with HVs involved. Adding BlaFT vehicles increases the efficiency of traffic as a whole by up to 34% over HVs alone. 

   more » « less