The ethical decisions behind the acquisition and analysis of audio, video or physiological human data, harnessed for (deep) machine learning algorithms, is an increasing concern for the Artificial Intelligence (AI) community. In this regard, herein we highlight the growing need for responsible, and representative data collection and analysis, through a discussion of modality diversification. Factors such as Auditability, Benchmarking, Confidence, Data-reliance, and Explainability (ABCDE), have been touched upon within the machine learning community, and here we lay out these ABCDE sub-categories in relation to the acquisition and analysis of multimodal data, to weave through the high priority ethical concerns currently under discussion for AI. To this end, we propose how these five subcategories can be included in early planning of such acquisition paradigms.
Abstract Big Data is a digital phenomenon that enables the collection and use of massive amounts of data derived from both man and machine. This data is characterized in terms of its volume, variety, velocity, veracity, variability, and its complexity. While Big Data allows firms to rapidly capture, analyze, and exploit information, it can also enable access to data that compromises an individual's privacy. And this can happen either deliberately or inadvertently. Either way, Big Data fosters a discussion of ethical issues relative to the sharing and usage of data. Ethical debates are typically articulated within the context of ethical theories. These theories help to frame our understanding of moral issues. Their use affords insight into the context and the logic of the moral arguments being presented, thereby providing us with a rational mechanism by which to better evaluate whether an intended action or actual outcome is morally right or wrong. Four ethical theories are briefly reviewed in this paper: Kantianism, Utilitarianism, Social Contract Theory, and Virtue Theory. Each theory is than examined to show how it might be employed to examine Big Data issues. One of the most used sources of information for fast and flexible crisis information is social media or crowdsourced data, as the information is rapidly disseminated, can reach a large amount of target audience and covers a wide variety of topics. However, the agility that these new methodologies enable comes at a price: ethics and privacy. This paper presents an analysis of the ethical risks and implications of using automated system that learn from social media data to provide intelligence in crisis management. The paper presents a short overview on the use of social media data in crisis management to then highlight ethical implication of machine learning and social media data using an example scenario. In conclusion general mitigation strategies and specific implementation guidelines for the scenario under analysis are presented. 