Category: Technology

Democratic Privacy Reform

If you aren’t familiar with the issues surrounding personal data collection by corporate tech giants and online privacy, I recommend you flip through Amnesty International’s publication Surveillance Giants: How the Business Model of Google and Facebook Threatens Human Rights. I would also suggest reading A Contextual Approach to Privacy Online by Helen Nissenbaum if you are interested in further discussions on the future of data collection. Actually, even if you are familiar with these issues, read them anyway because they are very interesting and you may learn something new.

Both articles offer interesting suggestions for governments and corporations to ensure online privacy is protected, and it is clear top-down approaches are necessary for upholding human rights. Substantial effort will be required for full corporate compliance however, as both law and computer systems need updating to better respect user data. While these measures ensure ethical responsibilities are directed to the appropriate parties, a complementary bottom-up approach may be required as well. There is great potential for change if citizens were to engage with this issue and help one another better understand the importance of privacy. A democratic strategy for protecting online human rights is possible, but it seems quite demanding considering this work is ideally performed voluntarily. Additionally, I fear putting this approach into practice is an uphill epistemic battle; many individuals aren’t overly bothered by surveillance. Since the issue is complex and technological, it is difficult to understand resulting in little concern due to the lack of perceived threat. Thus, there will always be a market for the Internet of Things. Moreover, advertising revenue provides little incentive for corporations to respect user data, unless a vocal group of protesters is able to substantially threaten their public image. Enacting regulatory laws may be effective for addressing human rights issues but the conflict between governments and companies is likely to continue under the status quo. Consumers who enjoy these platforms and products face a moral dilemma: is this acceptable if society and democracy is negatively impacted? Can ethical considerations regarding economic externalities help answer this question? If not, are there other analogous ethical theories which may be appropriate for questions regarding the responsibilities of citizens? If activists and ethicists are interested in organizing information and materials for empowering voters and consumers, these challenges will need practical and digestible answers.

Works Cited

Amnesty International. Surveillance Giants: How the Business Model of Google and Facebook Threatens Human Rights. Research article, amnesty.org/en/documents/pol30/1404/2019/en/, 2019.

Nissenbaum, Helen. “A contextual approach to privacy online.” Daedalus 140.4 (2011): 32-48.

AI and the Responsibility Gap

This week we are talking about the responsibility gap that arises from deep learning systems. We read Mind the gap: responsible robotics and the problem of responsibility by David Gunkel along with Andreas Matthias’ article The responsibility gap: Ascribing responsibility for the actions of learning automata.

It seems the mixture of excitement and fear surrounding the rise of autonomous agents may be the result of challenges to our intuitions on the distinction between objects and subjects. This new philosophical realm can be analyzed from a theoretical level, involving ontological and epistemological questions, but these issues can also be examined through a practical lens as well. Considering there may be a substantial amount of debate on the ontological status of various robots and AIs, it might be helpful to consider issues on morality and responsibility as separate to the theoretical questions, at least for now. The reason for this differentiation is to remain focused on protecting users and consumers as new applications of deep learning continue to modify our ontological foundations and daily life. Although legislative details will depend on the answers to theoretical questions to some degree, there may be existing approaches to determining responsibility that can be altered and adopted. Just as research and development firms are responsible for the outcomes of their products and testing procedures (Gunkel 12), AI companies too will likely shoulder the responsibility for unintended and unpredictable side-effects of their endeavours. The degree to which the organization can accurately determine the responsible individual(s) or components will be less straightforward than it may have been historically, but this is due to the complexity of the tools we are currently developing. We are no longer mere labourers using tools for improved efficiency (Gunkel 2); humans are generating technologies which are on the verge of possessing capacities for subjectivity. Even today, the relationship between a DCNN and its creators seems to have more in common with a child-parent relationship than an object-subject relationship. This implies companies are responsible for their products even when they misbehave, as the debacle surrounding Tay.ai demonstrates (Gunkel 5). It won’t be long, however, before we outgrow these concepts and our laws and regulations are challenged yet again. In spite of this, it is not in our best interest to wait until theoretical questions are answered before drafting policies aimed at protecting the public.

Works Cited

Gunkel, David J. “Mind the gap: responsible robotics and the problem of responsibility.” Ethics and Information Technology (2017): 1-14.

Is Opacity a Fundamental Property of Complex Systems?

While operational opacity generated by machine learning algorithms presents a wide range of problems for ethics and computer science (Burrell 10), one type in particular may be unavoidable due to the nature of complex processes. The physical underpinnings of functional systems may be difficult to understand because of the way data is stored and transmitted. Just as patterns of neural activity seem conceptually distant from first-person accounts of subjective experiences, the missing explanation for why or how a DCNN arrives at a particular decision may actually be a feature of the system rather than a bug. Systems capable of storing or processing large amounts of data may only be capable of doing so because of the way nested relationships are embedded in the structure. Furthermore, many of the human behaviours or capacities researchers are trying to understand and copy are both complex and emergent, making them difficult to fully trace back to the physical level of implementation. When we do, it often looks strange and quite chaotic. For example, molecular genetics suggests various combinations of nucleotides give rise to different types of cells and proteins, each with highly specialized and synergistic functions. Additionally, complex phenotypes like disease dispositions are typically the result of many interacting genotypic factors in conjunction with the presence of certain environmental variables. If it turns out to be the case that a degree of opacity is a necessary component of convoluted functionality, we may need to rethink our expectations of how ethics can inform the future of AI development.

Works Cited

Burrell, Jenna. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data & Society 3.1 (2016).

Addiction by Design: Candy Crush et al.

For class this week, we read the first four chapters of Natasha Schull’s book Addition by Design. I think the goal was to consider the similarities and differences between slot machines and gaming applications on handheld devices.

While the two addictions are comparable despite their differences in gameplay format, apps like Candy Crush have found profitable solutions to their unique problems. Developers expect players to “leave their seats” as cellphone use generally orbits around other aspects of daily life. While “time on device” (58) is surely an important part of app design, creating incentives for users to return are also significant. Though this may be accomplished in a number of ways, a common strategy is to generate frequent notifications to both remind and seduce users back to their flow state (49). Overall, the approach may seem less inviting than sounds and lights but its ability to display explicit directions may be effective. Text has the ability to specify rewards if the user opens the app right then and there. A pay structure involving varying wait times may also push users to pay for the ability to return to “the zone” (2). This may take the form of watching an advertisement or being disallowed to play for intervals from an hour to a day, sufficiently frustrating users to pay to continue playing. Similarly to embedding ATMs in slot machines (72), app stores with saved credit card information allow developers to seamlessly lead users to the ‘purchase’ button, quickly increasing revenue. Financial transactions thinly disguised as a part of the game offer a new way to siphon money from vulnerable individuals, especially parents of children with access to connected devices. Additionally, gaming apps are typically weakly associated with physical money like bills and coins, unlike slot machines from mid 20th century (62), perhaps making it easier for consumers to pay without drawing their attention to the movement of money. This brief analysis suggests the nature of gambling is evolving by modifying existing modes of persuasion and adapting to new technological environments.

One large concern, however, arises from where this money goes; while governmental agencies oversee regulations (91) and collect revenue (5) to fund programs and projects, private companies simply collect capital. This carries severe implications for individuals, communities and economies as this alternative stream of income dries up. Therefore, it could be suggested that state and provincial legislators should consider addressing this issue sooner than later.

Works Cited

Schüll, Natasha Dow. Addiction by design: Machine gambling in Las Vegas. Princeton University Press, 2014.

Algorithmic Transparency and Social Power

This term I’m taking the course Science and Ethics, and this week we read Langdon Winner’s 1980 article “Do Artifacts have Politics?” along with a paper from 2016 published by Brent Daniel Mittelstadt and colleagues titled “The ethics of algorithms: Mapping the debate.” We are encouraged to do weekly responses, and considering the concerning nature of what these articles are discussing, thought it should be presented here. There is definitely a lot that could be expanded upon, which I might consider doing at a later time.

Overall, the two articles suggested risks of discriminatory outcomes are an aspect of technological advancements, especially when power imbalances are present or inherent. The paper The ethics of algorithms: Mapping the debate focused particularly on algorithmic design and its current lack of transparency (Mittelstadt 6). The authors mention how this is an epistemic concern, as developers are unable to determine how a decision is reached, which leads to normative problems. Algorithmic outcomes potentially generate discriminatory practices which may generalize and treat groups of people erroneously (Mittelstadt 5). Thus, given the elusive epistemic nature of current algorithmic design, individuals throughout the entire organization can truthfully claim ignorance of their own business practices. Some may take advantage of this fact. Today, corporations that manage to successfully integrate their software into the daily life of many millions of users have little incentive to change, due to shareholder desires for financial growth. Until the system which implicitly suggests companies can simply pay a fee, in the form of legal settlements outside of court, to act unethically, this problem is likely to continue to manifest. This indeed does not inspire confidence for the future of AI as we hand over our personal information to companies and governments (Mittelstadt 6).

Langdon Winner’s on whether artifacts have politics provides a compelling argument for the inherently political nature of our technological objects. While this paper may have been published in 1980, its wisdom and relevance can be readily applied to contemporary contexts. Internet memes even pick up on this parallel; one example poses as a message from Microsoft stating those who program open-source software are communists. While roles of leadership are required for many projects or organizations (Winner 130), inherently political technologies have the hierarchy of social functioning as part of their conceptual foundations, according to Winner (133). The point the author aims to stress surrounds technological effects which impede social functioning (Winner 131), a direction we have yet to move away from considering the events leading up to and following the 2016 American presidential election. If we don’t strive for better epistemic and normative transparency, we will be met with authoritarian outcomes. As neural networks continue to creep into various sectors of society, like law, healthcare, and education, ensuring the protection of individual rights remains at risk.

Works Cited

Mittelstadt, Brent Daniel, et al. “The ethics of algorithms: Mapping the debate.” Big Data & Society 3.2 (2016): 1-21.

Winner, Langdon. “Do artifacts have politics?.” Daedalus 109.1 (1980): 121-36.

Programming Emotions

Last summer, I was introduced to the world of hobby robotics and began building an obstacle-avoidance bot as a way to learn the basics. Once classes started last September, all projects were set aside until I graduated, allowing me to focus on school. Now that I have free time, I’ve been thinking about what kind of robot to build next. It will probably still have wheels and an ultrasonic sensor, but I want it to behave based on its internal environment as well as its external environment. Not only will it detect objects in its path, but it will also move about based on its mood or current emotional state. For example, if it were to be afraid of loud noises, it would go to “hide” against a nearby object. This specific functionality would require the robot have a microphone to detect sounds, and is something I have been thinking of adding. Otherwise, the only input the robot has is object-detection, and producing or calculating emotions based on the frequency of things in its path is kind of boring. I have also been interested in operationalizing, codifying, and programming emotions for quite a while now, and this project would be a great place to start.

One helpful theory I came across is the Three-Factor Theory (3FT) developed by Mehrabian and Russell in 1974 (Russell and Mehrabian 274). It describes emotions as ranging through a three-dimensional space consisting of values for pleasure, arousal, and dominance. For example, a state of anger is associated with -.68 for pleasure, +.22 for arousal, and +.10 for dominance (Russell and Mehrabian 277). After mulling on these averages for a second, I feel these are fairly reflective of general human nature, but let’s not forget these values are dependent on personality and contextual factors too. However, the notion of ‘dominance’ doesn’t feel quite right, and I wonder if a better paradigm could take its place. Personally, the idea of being dominant or submissive is quite similar to the approach/avoidance dichotomy used in areas of biology and psychology. ‘Dominance’ is inherently tied to social situations, and a broader theory of emotion must account for non-social circumstances as well. The compelling argument from the approach/avoidance model centers around hedonism, motivation, and goal acquisition; if a stimulus is pleasurable or beneficial, individuals are motivated to seek it out, while undesirable or dangerous stimuli are avoided in order to protect oneself (Elliot 171). Furthermore, this also works well with the Appraisal Theory of emotion, as it argues that affective states indicate an individual’s needs or goals (Scherer 638). Therefore, I will be using a value range based on approach/avoidance rather than dominance. While human emotions tend to involve much more than a simple judgement about a situation, the Appraisal Theory should suffice for a basic robot. One last modification I would like to make in my version of the 3FT is changing ‘pleasure’ to ‘valence’. This is merely to reflect the style of language used in current psychological literature, where positive values are associated with pleasure and negative values are associated with displeasure. I also like this because robots don’t feel pleasure (yet?) but they are capable of responding based on “good” and “bad” types of stimuli. ‘Arousal’ is perfectly fine as it is, as it reflects how energetic or excited the individual is. For example, being startled results in high arousal due to the relationship between the amygdala, hypothalamus, and other local and distal regions in the body, which typically prepare the individual to run or fight (Pinel 453-454).

To summarize, the three factors I will be using are valence, arousal, and approach/avoidance. As much as I would love to find a term to replace ‘approach/avoidance’, for the sake of a nice acronym, I have yet to find one which encapsulates the true nature of the phenomenon. Anyway, this modified 3FT seems to be a good start for developing emotional states in a simple robot, especially if it only receives a narrow range of sensory input and does not perform any other sophisticated behaviours. While this robot will possess internal states, it won’t be able to reflect upon them nor have any degree of control over them. Heck, I won’t even be using any type of AI algorithms in this version. So if anyone is spooked by a robot who feels, just know that it won’t be able to take over the world.

Works Cited

Elliot, Andrew J. “Approach and avoidance motivation and achievement goals.” Educational psychologist 34.3 (1999): 169-189.

Pinel, John PJ. Biopsychology. Boston, MA: Pearson, 2011.

Russell, James A., and Albert Mehrabian. “Evidence for a three-factor theory of emotions.” Journal of research in Personality 11.3 (1977): 273-294.

Scherer, Klaus R. “Appraisal theory.” Handbook of cognition and emotion (1999): 637-663.