Principles to Practices for Responsible AI: Closing the Gap

Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. In turn, we argue that an impact assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. Finally, to help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.

Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed

In the last decade, social networks became most popular medium for communication and interaction. As an example, micro-blogging service Twitter has more than 200 million registered users who exchange more than 65 million posts per day. Users express their thoughts, ideas, and even their intentions through these tweets. Most of the tweets are written informally and often in slang language, that contains misspelt and abbreviated words. This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques. It starts by presenting the method we used to construct our own dataset from extracted Twitter feeds. Following that, we present two techniques of feature selection followed by classification. In the first technique, we use Information Gain as a one-phase feature selection, followed by supervised classification algorithms. In the second technique, we use a hybrid approach based on forward feature selection algorithm in which two feature selection techniques employed followed by classification algorithms. We examine these two techniques with four classification algorithms. We evaluate them using our own dataset, and we critically review the results.

Turing Test Revisited: A Framework for an Alternative

This paper aims to question the suitability of the Turing Test, for testing machine intelligence, in the light of advances made in the last 60 years in science, medicine, and philosophy of mind. While the main concept of the test may seem sound and valid, a detailed analysis of what is required to pass the test highlights a significant flow. Once the analysis of the test is presented, a systematic approach is followed in analysing what is needed to devise a test or tests for intelligent machines. The paper presents a plausible generic framework based on categories of factors implied by subjective perception of intelligence. An evaluative discussion concludes the paper highlighting some of the unaddressed issues within this generic framework.

Cognitive Systems Approach to Smart Cities

In our connected world, services are expected to be delivered at speed through multiple means with seamless communication. To put it in day to day conversational terms, 'there is an app for it' attitude prevails. Several technologies are needed to meet this growing demand and indeed these technologies are being developed. The first noteworthy is Internet of Things (IoT), which is in itself coupled technologies to deliver seamless communication with 'anywhere, anytime' as an underlying objective. The 'anywhere, anytime' service delivery paradigm requires a new type of smart systems in developing these services with better capabilities to interact with the human user, such as personalisation, affect state recognition, etc. Here enter cognitive systems, where AI meets cognitive sciences (e.g. cognitive psychology, linguistics, social cognition, etc.). In this paper we will examine the requirements imposed by smart cities development, e.g. intelligent logistics, sensor networks and domestic appliances connectivity, data streams and media delivery, to mention but few. Then we will explore how cognitive systems can meet the challenges these requirements present to the development of new systems. Throughout our discussion here, examples from our recent and current projects will be given supplemented by examples from the literature.