machine learning

The algorithms technology rely upon create a new type of curated media that can undermine the fairness and quality of political discourse.

The Facebook Wall, by René C. Nielsen (Flickr).

A central ideal of democracy is that political discourse should allow a fair and critical exchange of ideas and values. But political discourse is unavoidably mediated by the mechanisms and technologies we use to communicate and receive information—and content personalisation systems (think search engines, social media feeds and targeted advertising), and the algorithms they rely upon, create a new type of curated media that can undermine the fairness and quality of political discourse. A new article by Brent Mittlestadt explores the challenges of enforcing a political right to transparency in content personalisation systems. Firstly, he explains the value of transparency to political discourse and suggests how content personalisation systems undermine open exchange of ideas and evidence among participants: at a minimum, personalisation systems can undermine political discourse by curbing the diversity of ideas that participants encounter. Second, he explores work on the detection of discrimination in algorithmic decision making, including techniques of algorithmic auditing that service providers can employ to detect political bias. Third, he identifies several factors that inhibit auditing and thus indicate reasonable limitations on the ethical duties incurred by service providers—content personalisation systems can function opaquely and be resistant to auditing because of poor accessibility and interpretability of decision-making frameworks. Finally, Brent concludes with reflections on the need for regulation of content personalisation systems. He notes that no matter how auditing is pursued, standards to detect evidence of political bias in personalised content are urgently required. Methods are needed to routinely and consistently assign political value labels to content delivered by personalisation systems. This is perhaps the most pressing area for future work—to develop practical methods for algorithmic auditing. The right to transparency in political discourse may seem unusual and farfetched. However, standards already set by the U.S. Federal Communication Commission’s fairness doctrine—no longer in force—and the British Broadcasting Corporation’s fairness principle both demonstrate the importance of the idealised version of political discourse described here. Both precedents…

Online support groups are one of the major ways in which the Internet has fundamentally changed how people experience health and health care.

Online forums are important means of people living with health conditions to obtain both emotional and informational support from this in a similar situation. Pictured: The Alzheimer Society of B.C. unveiled three life-size ice sculptures depicting important moments in life. The ice sculptures will melt, representing the fading of life memories on the dementia journey. Image: bcgovphotos (Flickr)

Online support groups are being used increasingly by individuals who suffer from a wide range of medical conditions. OII DPhil Student Ulrike Deetjen’s recent article with John Powell, Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis uses machine learning to examine the role of online support groups in the healthcare process. They categorise 40,000 online posts from one of the most well-used forums to show how users with different conditions receive different types of support. Online support groups are one of the major ways in which the Internet has fundamentally changed how people experience health and health care. They provide a platform for health discussions formerly restricted by time and place, enable individuals to connect with others in similar situations, and facilitate open, anonymous communication. Previous studies have identified that individuals primarily obtain two kinds of support from online support groups: informational (for example, advice on treatments, medication, symptom relief, and diet) and emotional (for example, receiving encouragement, being told they are in others’ prayers, receiving “hugs”, or being told that they are not alone). However, existing research has been limited as it has often used hand-coded qualitative approaches to contrast both forms of support, thereby only examining relatively few posts (<1,000) for one or two conditions. In contrast, our research employed a machine-learning approach suitable for uncovering patterns in “big data”. Using this method a computer (which initially has no knowledge of online support groups) is given examples of informational and emotional posts (2,000 examples in our study). It then “learns” what words are associated with each category (emotional: prayers, sorry, hugs, glad, thoughts, deal, welcome, thank, god, loved, strength, alone, support, wonderful, sending; informational: effects, started, weight, blood, eating, drink, dose, night, recently, taking, side, using, twice, meal). The computer then uses this knowledge to assess new posts, and decide whether they contain more emotional or informational support. With this approach we were able to determine the emotional or informational content of 40,000…