Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userToward Explainable Users: Using NLP to Enable AI to Understand Users Perceptions of Cyber Attacks
513
0
0.0
(
0
)
Added by
Akbar Siami Namin
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants. As an exploration of whether it is possible to explain users mental model and behavior through Artificial Intelligence (AI) techniques, the research team compared the card sorting data with the outputs of a number of Natural Language Processing (NLP) techniques with the goal of understanding how participants perceived and interpreted the consequences of cyber attacks written in natural languages. The results of the NLP-based exploration methods revealed an interesting observation implying that participants had mostly employed checking individual keywords in each sentence to group cyber attack consequences together and less considered the semantics behind the description of consequences of cyber attacks. The results reported in this paper are seemingly useful and important for cyber attacks comprehension from users perspectives. To the best of our knowledge, this paper is the first introducing the use of AI techniques in explaining and modeling users behavior and their perceptions about a context. The novel idea introduced here is about explaining users using AI.
rate research
Read More
Explainability of AI systems is critical for users to take informed actions and hold systems accountable. While opening the opaque box is important, understanding who opens the box can govern if the Human-AI interaction is effective. In this paper, we conduct a mixed-methods study of how two different groups of whos--people with and without a background in AI--perceive different types of AI explanations. These groups were chosen to look at how disparities in AI backgrounds can exacerbate the creator-consumer gap. We quantitatively share what the perceptions are along five dimensions: confidence, intelligence, understandability, second chance, and friendliness. Qualitatively, we highlight how the AI background influences each groups interpretations and elucidate why the differences might exist through the lenses of appropriation and cognitive heuristics. We find that (1) both groups had unwarranted faith in numbers, to different extents and for different reasons, (2) each group found explanatory values in different explanations that went beyond the usage we designed them for, and (3) each group had different requirements of what counts as humanlike explanations. Using our findings, we discuss potential negative consequences such as harmful manipulation of user trust and propose design interventions to mitigate them. By bringing conscious awareness to how and why AI backgrounds shape perceptions of potential creators and consumers in XAI, our work takes a formative step in advancing a pluralistic Human-centered Explainable AI discourse.
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.
The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear. We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We thus hope to advance and structure the dialogue in the HCI research community on supporting users in understanding intelligent systems.
The investigation of the browsing behavior of users provides useful information to optimize web site design, web browser design, search engines offerings, and online advertisement. This has been a topic of active research since the Web started and a large body of work exists. However, new online services as well as advances in Web and mobile technologies clearly changed the meaning behind browsing the Web and require a fresh look at the problem and research, specifically in respect to whether the used models are still appropriate. Platforms such as YouTube, Netflix or last.fm have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and platforms for browser games attracted whole new, particularly less tech-savvy audiences. Furthermore, advances in mobile technologies and devices made browsing on-the-move the norm and changed the user behavior as in the mobile case browsing is often being influenced by the users location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. To this end, DOBBS provides a browser add-on that users can install, which keeps track of their browsing behavior (e.g., how much time they spent on the Web, how long they stay on a website, how often they visit a website, how they use their browser, etc.). In this paper, we outline the motivation behind DOBBS, describe the add-on and captured data in detail, and present some first results to highlight the strengths of DOBBS.
AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers goals and then help them without being needlessly disruptive. We present AI-assisted design: a framework for creating such AI, built around generative user models which enable reasoning about designers goals, reasoning, and capabilities.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
