Welcome to the EI weekly round-up; a curation of quality posts to help you cut through the noise and get right to the heart of the discussion on AI and Tech Ethics.
Every Tuesday we publish a list of links to articles and debates that have happened over the past week in the community, allowing you to stay as up-to-date as possible on developments and facts. We will often link to arguments from all sides of the debate, even if the opinions may be controversial. We would like to mention, however, that EI does not endorse any of the information published, all links are reflections of the author's opinions and not that of Ethical Intelligence.
An ethically mindful approach to AI for health care
"Health-care systems worldwide face increasing demand, a rise in chronic disease, and resource constraints. At the same time, the use of digital health technologies in all care settings has led to an expansion of data. These data, if harnessed appropriately, could enable health-care providers to target the causes of ill-health and monitor the effectiveness of preventions and interventions. For this reason, policy makers, politicians, clinical entrepreneurs, and computer and data scientists argue that a key part of health-care solutions will be artificial Intelligence (AI), particularly machine learning."
From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy
"The word 'ethics' is under siege in technology policy circles. Weaponized in support of deregulation, self-regulation or handsoff governance, "ethics" is increasingly identified with technology companies' self-regulatory efforts and with shallow appearances of ethical behavior. So-called "ethics washing" by tech companies is on the rise, prompting criticism and scrutiny from scholars and the tech community at large. In parallel to the growth of ethics washing, its condemnation has led to a tendency to engage in "ethics bashing." This consists in the trivialization of ethics and moral philosophy now understood as discrete tools or pre-formed social structures such as ethics boards, self-governance schemes or stakeholder groups."
Artificial Intelligence, Values and Alignment
"This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify 'true' moral principles for AI; rather, it is to identify fair principles for alignment, that receive reflective endorsement despite widespread variation in people's moral beliefs."
Bias in Word Embeddings
"Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice. In this study, we provide a complete overview of bias in word embeddings."
The Road to Artificial Intelligence: An Ethical Minefield
"There are a number of ethical dilemmas woven inextricably into the field of Artificial Intelligence, many of which are often overlooked, even within the engineering community. Even the best intentions are often not enough to guarantee solutions free from unintended or undesired results, as humans can accidentally encode biases into AI engines and malicious actors can exploit flaws in models. In the short-term, accountability and transparency on behalf of tech companies is critical, as is vigilance on behalf of consumers."
Preparing For AI Ethics And Explainability In 2020
"People distrust artificial intelligence and in some ways this makes sense. With the desire to create the best performing AI models, many organizations have prioritized complexity over the concepts of explainability and trust. As the world becomes more dependent on algorithms for making a wide range of decisions, technologies and business leaders will be tasked with explaining how a model selected its outcome. "
The battle for ethical AI at the world’s biggest machine-learning conference
"Bias and the prospect of societal harm increasingly plague artificial-intelligence research — but it’s not clear who should be on the lookout for these problems."
The Ethical Upside to Artificial Intelligence
"if correctly designed, AI should clarify and amplify the ethical frameworks that U.S. military leaders already bring to war. "
The case for AI transparency requirements
"As AI technologies quickly and methodically climb out of the uncanny valley, customer service calls, website chatbots, and interactions on social media and in virtual reality may become progressively less evidently artificial."