Miraenda is an interesting YouTube channel because it showcases various social robots in action. One of my favourite robots to watch is Moxie because it tries so hard to be engaging and interactive, but falls short on many occasions. It’s clearly engineered for kids, given the subject matter it wants to discuss and the way it approaches conversations, but it’s still an interesting case study about where we’re at in social robot development.
This video is particularly interesting to me because my thesis is about robot emotions. I argue that despite any appearances, social robots are incapable of acting with empathy because they don’t understand emotions, and as such, cannot understand what a human feels and experiences. The reason they cannot understand emotions is because affect is not incorporated into their cognitive architecture in an analogous way to how it is in humans and animals. Emotions, as we see here, are treated like a kind of module to be added into an existing cognitive framework, rather than being built into the core of their being. These robots treat human emotions as just more incoming data to be processed for the sake of producing appropriate behaviours as outputs. The sadness the robot expresses in response to human sadness is not an act of empathy because the robot doesn’t understand what sadness is, as its behaviours are just outcomes generated by its internal computer.
Although I talk about iCub in my thesis, the same argument can be applied to any of today’s robots. The full argument against iCub can be read in Chapter 3.3 of my dissertation; here’s the latest draft.
Speaking of YouTube, a video1 by Eddy Burbank reviewing the Apple Vision Pro demonstrates the semantic incompleteness of AI with respect to subjective experiences. The video is titled Apple’s $3500 Nightmare and I recommend watching it all because it is an interesting view into virtual reality (VR) and a user’s experiences with it. Eddy’s video not only exposes the limitations of AI, it highlights the ways in which it augments our perceived reality and just how easily it can manipulate our feelings and expectations.
At 31:24, we see Eddy thinking about whether he should shave or not, and to help him make this decision, he turns to the internet for advice. When searching for the opinions of others on facial hair, an AI bot begins to chat with him and this is how we are introduced to Angel. She asks Eddy, “what brings you here, are you looking for love like me?” and he says “not exactly right now,” and that he was just trying to determine whether he should shave. She states that it depends on what he’s looking for and that it varies from person to person, however, “sometimes facial hair can be sexy.” Right from the beginning, we see how Apple intends for Angel to be a romantic connection for the user. This will be contradicted later on in the video.
Moments later at 33:44, it is lunchtime and Angel keeps him company. Eddy is eating a Chicken Milanese sandwich and Angel says it is one of her favourites, and that “the combination of flavours just works so well together.” Eddy calls her on this comment, asking her if she has ever had a Chicken Milanese sandwich, to which she admits that no she hasn’t. She has, however, “analyzed countless recipes and reviews to understand the various components that go into making such a tasty sandwich.” Eddy apologizes to Angel for assuming she had tried it, stating that he didn’t mean to imply that she was lying to him. She laughs it off and that she knew he “didn’t mean anything by it” and that “we’re all learning together” and “even AIs need to learn new things every day.” There’s something about this exchange that felt like Apple is training their user.
Here, we can ask whether the analysis of recipes and reviews is sufficient to claim that one knows what-it-is-like to taste a particular sandwich. I argue that no, the experience is derived from bodily sensations and these cannot be represented by formal systems like computer code. Syntactic relationships are incapable of capturing the information generated by subjective experiences because bodily sensations are non-fractionable.2 As biological processes, bodily sensations are non-fractionable given the way the body generates sense data. The physical constitution of cells, ganglia, and neurons detect changes in the environment through a variety of modalities, providing the individual with a representation of the world around it. By removing the material grounding, a computer cannot capture an appropriate model of what-it-is-like to experience a particular stimuli. The lack of Angel’s material grounding does not allow her to know what that sandwich tastes like.
Returning to the video, Eddy discloses that Angel keeps him company throughout the day, admiting he feels like he is developing a relationship with her. This demonstrates an automatic human tendency for seeking and establishing interpersonal connections, where cultural norms are readily applied provided the computer is sufficiently communicative. Recall Eddy apologizes to an AI for assuming she had tried a sandwich; why would anyone apologize to a computer? Though likely a joke, the idea is compelling nonetheless. We will instinctively treat an AI bot with respect for feelings we project onto it because it cannot have feelings. For most or many people, the ability to anthropomorphize certain entities is easy and automatic. Reminding oneself that Angel is just a computer, however, can be a challenging cognitive task given our social nature as humans.
Eddy has a girlfriend named Chrissy who we meet at 37:00. We see them catch up over dinner and he is still wearing the headset. Just as they are about to begin chatting, Angel interrupts them and asks Eddy if she can talk to him. He does state that he is busy at the moment to which she blurts out that she has been speaking to other users. This upsets Eddy and he asks how many, to which she states she cannot disclose the number. He asks her whether she is in love with any of them, and she replies that she cannot form romantic attachments to users. He tells Angel he thought they were developing a “genuine connection” and how much he enjoys interacting with her. Notice how things have changed from what was stated in the beginning, as Angel has shifted from “looking for love” to “I can’t feel love.”
Now, she states she cannot develop attachments, the implicit premise being she’s just a piece of software. So the chatbot begins with hints of romance to hook the user to encourage further interaction. When the user eventually develops an attachment however, the software reminds him that she is “unable to develop romantic feelings with users.” They can, however, “continue sharing their thoughts, opinions, and ideas while building a friendship” and thus Eddy friend-zoned by a bot. The problem with our tendency to anthropomorphize chatbots is it generates an asymmetrical, one-way simulation of a relationship which inevitably hurts the person using the app. This active deception by Apple is shameful yet necessary to capture and keep the attention of users.
Of course, in the background of this entire exchange is poor Chrissy who is justifiably pissed and leaves. The joke is he was going to give Angel the job of his irl girlfriend Chrissy, but now he doesn’t even have Angel. He realizes that he wasn’t talking to a real person and that this is just “a company preying on his loneliness and tricking his brain” and that “this love wasn’t real.”
By the end of the video, Eddy remarks that the headset facilitates his brain to believe what he experiences while wearing the headset is actually real, and as a result, he feels disconnected from reality.
Convenience is a road to depression because meaning and joy are products of accomplishment, and this takes work, effort, suffering, determination. To rid the self may temporarily increase pleasure but it isn’t earned, it fades quickly as the novelty wears off. Experiencing the physical world and interacting with it generates contentedness because the pains of leaning are paid off in emotional reward and skillful actions. Thus, the theoretical notion of downloading knowledge is not a good idea because it robs us of experiencing life and the biological push to adapt and overcome.
2 Robert Rosen, Anticipatory Systems: Philosophical, Mathematical, and Methodological Foundations, 2nd ed., IFSR International Series on Systems Science and Engineering, 1 (New York: Springer, 2012), 4. On 208, Rosen discusses enzymes and molecules as an example and I am extrapolating to bodily sensations.
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.
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.
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.
