/pdf/living-a-feminist-life-14tpwoqhex.pdf

Living a feminist life

As firms are moving towards data-driven decision making, they are facing an emerging problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially-biased outcomes, thereby ...

Algorithmic bias: review, synthesis, and future research directions

The human-computer interaction community has made some efforts toward racial diversity, but the outcomes remain meager. We introduce critical race theory and adapt it for HCI to lay a theoretical basis for race-conscious efforts, both in research and within our community. Building on the theory's original tenets, we argue that racism is pervasive in everyday socio-technical systems; that the HCI community is prone to "interest convergence", where concessions to inclusion require benefits to those in power; and that the neoliberal underpinnings of the technology industry itself propagate racism. Critical race theory uses storytelling as a means to upend deep-seated assumptions, and we relate several personal stories to highlight ongoing problems of race in HCI. The implications: all HCI research must be attuned to issues of race; participation of underrepresented minorities must be sought in all of our activities; and as a community, we cannot become comfortable while racial disparities exist.

Critical Race Theory for HCI

It has become trivial to point out that algorithmic systems increasingly pervade the social sphere. Improved efficiency-the hallmark of these systems-drives their mass integration into day-to-day life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm. In particular, a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and harm are brought to the fore, most of the solutions on offer (1) revolve around technical solutions and (2) do not center disproportionally impacted communities. This paper proposes a fundamental shift-from rational to relational-in thinking about personhood, data, justice, and everything in between, and places ethics as something that goes above and beyond technical solutions. Outlining the idea of ethics built on the foundations of relationality, this paper calls for a rethinking of justice and ethics as a set of broad, contingent, and fluid concepts and down-to-earth practices that are best viewed as a habit and not a mere methodology for data science. As such, this paper mainly offers critical examinations and reflection and not "solutions."

Algorithmic injustice: a relational ethics approach.

In 2008, Cathy O’Neil, a data scientist at the hedge fund D.E. Shaw, watched as the entire financial sector around her imploded.1 The financial crisis gave O’Neil, a self-proclaimed math nerd with a Ph.D., a front row seat to the weaponization of mathematics. During the financial crisis, complex mathematical models designed and marketed to be superior to human ability and beyond the confines of human subjectivity turned out to be farces—layers of false assumptions coated in difficult-to-understand layers of mathematics. In order to dupe both the public and themselves, financial analysts at banks used models and algorithms as a justification for their subjective and flawed inputs. Ten years after the financial crisis, O’Neil sees these nefarious models infiltrating every aspect of modern life. These “Weapons of Math Destruction”—as O’Neil calls them—are (1) “opaque,” (2) “beyond dispute or appeal,” and (3) disproportionally impact the underprivileged.2 These Weapons of Math Destruction, or WMDs, replace the role of traditional subjective decision makers and cause those affected by their decisionmaking to adjust their ways of life to the models. In Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, O’Neil highlights different WMDs that have infiltrated various areas of American life.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

There are increasing expectations that algorithms should behave in a manner that is socially just. We consider the case of image tagging APIs and their interpretations of people images. Image taggers have become indispensable in our information ecosystem, facilitating new modes of visual communication and sharing. Recently, they have become widely available as Cognitive Services. But while tagging APIs offer developers an inexpensive and convenient means to add functionality to their creations, most are opaque and proprietary. Through a cross-platform comparison of six taggers, we show that behaviors differ significantly. While some offer more interpretation on images, they may exhibit less fairness toward the depicted persons, by misuse of gender-related tags and/or making judgments on a person’s physical appearance. We also discuss the difficulties of studying fairness in situations where algorithmic systems cannot be benchmarked against a ground truth.

/pdf/fairness-in-proprietary-image-tagging-algorithms-a-cross-1vkvlrhe78.pdf

Fairness in Proprietary Image Tagging Algorithms: A Cross-Platform Audit on People Images

While professionals are increasingly relying on algorithmic systems for making a decision, on some occasions, algorithmic decisions may be perceived as biased or not just. Prior work has looked into the perception of algorithmic decision-making from the user's point of view. In this work, we investigate how students in fields adjacent to algorithm development perceive algorithmic decisionmaking. Participants (N=99) were asked to rate their agreement with statements regarding six constructs that are related to facets of fairness and justice in algorithmic decision-making in three separate scenarios. Two of the three scenarios were independent of each other, while the third scenario presented three different outcomes of the same algorithmic system, demonstrating perception changes triggered by different outputs. Quantitative analysis indicates that a) 'agreeing' with a decision does not mean the person 'deserves the outcome', b) perceiving the factors used in the decision-making as 'appropriate' does not make the decision of the system 'fair' and c) perceiving a system's decision as 'not fair' is affecting the participants' 'trust' in the system. In addition, participants found proportional distribution of benefits more fair than other approaches. Qualitative analysis provides further insights into that information the participants find essential to judge and understand an algorithmic decision-making system's fairness. Finally, the level of academic education has a role to play in the perception of fairness and justice in algorithmic decision-making.

I agree with the decision, but they didn't deserve this: Future Developers' Perception of Fairness in Algorithmic Decisions

Crowdsourcing plays a key role in developing algorithms for image recognition or captioning. Major datasets, such as MS COCO or Flickr30K, have been built by eliciting natural language descriptions of images from workers. Yet such elicitation tasks are susceptible to human biases, including stereotyping people depicted in images. Given the growing concerns surrounding discrimination in algorithms, as well as in the data used to train them, it is necessary to take a critical look at this practice. We conduct experiments at Figure Eight using a controlled set of people images. Men and women of various races are positioned in the same manner, wearing a grey t-shirt. We prompt workers for 10 descriptive labels, and consider them using the human-centric approach, which assumes reporting bias. We find that “what’s worth saying” about these uniform images often differs as a function of the gender and race of the depicted person, violating the notion of group fairness. Although this diversity in natural language people descriptions is expected and often beneficial, it could result in automated disparate impact if not managed properly.

/pdf/how-do-we-talk-about-other-people-group-un-fairness-in-2up4af3acg.pdf

How Do We Talk about Other People? Group (Un)Fairness in Natural Language Image Descriptions

Image analysis algorithms have become an indispensable tool in our information ecosystem, facilitating new forms of visual communication and information sharing. At the same time, they enable large-scale socio-technical research which would otherwise be difficult to carry out. However, their outputs may exhibit social bias, especially when analyzing people images. Since most algorithms are proprietary and opaque, we propose a method of auditing their outputs for social biases. To be able to compare how algorithms interpret a controlled set of people images, we collected descriptions across six image tagging algorithms. In order to compare these results to human behavior, we also collected descriptions on the same images from crowdworkers in two anglophone regions. The dataset we present consists of tags from these eight taggers, along with a typology of concepts, and a python script to calculate vector scores for each image and tagger. Using our methodology, researchers can see the behaviors of the image tagging algorithms and compare them to those of crowdworkers. Beyond computer vision auditing, the dataset of humanand machine-produced tags, the typology, and the vectorization method can be used to explore a range of research questions related to both algorithmic and human behaviors.

/pdf/social-b-eye-as-human-and-machine-descriptions-of-people-97cp3dj6cm.pdf

Social B(eye)as: Human and Machine Descriptions of People Images

Image analysis algorithms have been a boon to personalization in digital systems and are now widely available via easy-to-use APIs. However, it is important to ensure that they behave fairly in applications that involve processing images of people, such as dating apps. We conduct an experiment to shed light on the factors influencing the perception of "fairness." Participants are shown a photo along with two descriptions (human- and algorithm-generated). They are then asked to indicate which is "more fair" in the context of a dating site, and explain their reasoning. We vary a number of factors, including the gender, race and attractiveness of the person in the photo. While participants generally found human-generated tags to be more fair, API tags were judged as being more fair in one setting - where the image depicted an "attractive," white individual. In their explanations, participants often mention accuracy, as well as the objectivity/subjectivity of the tags in the description. We relate our work to the ongoing conversation about fairness in opaque tools like image tagging APIs, and their potential to result in harm.

What Makes an Image Tagger Fair

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