Introduction
Keep scrolling on the screen, contents that popped up are all related and exciting to me. Do you feel the same way? We use keywords on the search engine to find what we want, while Artificial Intelligence labels you as a frequent user. What keywords do you think will relate to your online identity in the digital space? Why and how do online platforms label users?
Artificial intelligence, such as Maps and Recommendation Algorithms, enables an efficient and easily accessible information selection that indicates the distribution and consumption of the automated culture (Andrejevic, 2019). Nowadays, the smartphone is the most common and significant carrier for Artificial intelligence presented throughout the daily software. Taking YouTube, eBay, and Amazon as examples, recommendation services from the platform gradually impact the audience’s familiarity with the suggested product that the algorithm considered we might be engaging with (Pasquale, 2015).
As the foundation of Artificial intelligence, Algorithmalgorithm is grown under the big-data Society and is an automatic process specific for calculating activities. AI contains logical codification that can use data-based algorithms to process massive machine learning activities such as image recognition and information extraction. As I mentioned above, the Algorithmalgorithm is used for solving the problem. Yet the selected pieces of information received can affect how we see the world and influence behavior (Just & Latzer, 2017).
Artificial intelligence is often considered an accelerating technology that already has a better working performance than humans. At the 2017 Future of Go Summit, Google’s AI AlphaGo defeated the world Go champion Ke Jie by just a half-point higher(Byford, 2017). Big data is developed to create an unexpected capacity for AI to process abilities or activities that humans couldn’t reach. Small information from images, voice, text, and video are fully presented and shaped their value through social media, providing an attribute to the digital press (Just & Latzer, 2017). Ethical issues are raised when this power of technology is both an opportunity and challenge for Society and individuals.
Ethical issues on People
Shaping behavior
Algorithmic selection applications include diverse functional types such as search, surveillance, filtering, allocation, and recommendation. Powerful applications such as Google and Baidu provide convenient online space to navigate users to reach out vast, selected information. When you use software such as Spectre Camera, the performance made on the software will be observed by AI and machine learning to merge a perfect image. Filtering content for the child, allocating advertisements for relating purchases, or recommending gripping drama or video games are all using technologies based on users’ preferences to determine what users will see.
Primarily serving the state and the corporations, the AI system is a tool not only for producing cultural content but also for distribution and consumption for the economic and political sector (Crawford, 2021). The commercial content combines characteristics of profitable and relatable is filled with entire websites such as Amazon and eBay. The user historically purchased a long dress. The surveillance engine will remember the purchase performance or record items that the user has clicked on. AI will use these data to filter massive volumes of products information and then formulate possible attractive things to post in the recommendation section. Somehow, this new and related information convinced the user purchased a new item. Despite the consumption behavior, commercial news and celebrity content might also be pushed to your face and increase the level of familiarity with the product. The ultra aim of the media is to create and maintain people’s desires through images (Ewen & Ewen, as cited in Lacković, 2021).
AI distributes information with beam transmission, considering the user as an isolated target within the digital environment. Think about how you get information within the physical environment. When citizen lives in the community, we cannot only read the newsletter but also stick advertisement on the wall. But also can get in touch with people who live or works near us with different social identity, for instance, teacher, businessman and relatives. The character of community life is that using built-in resources that strengthen the form of interdependence (Andrejevic, 2019). Considering the political topic you viewed online or debated on Twitter, it can also be discussed with teachers, businessmen, and relatives. Compared with the customized information online, the interdependent community relation introduces a border sense of reliance and interconnection (Andrejevic, 2019). When individual needs to judge, the problem of a “filter bubble” also prevents you from getting critical analysis from different angles. It increases individualization and keeps individuals away from developing comprehensive thinking.
Labor
As long as the job can be fully automatable, AI could be built to do it better and cheaper than a human being (Grace et al., 2018). People who work as car washers or models in an easily replaceable job by machines are always the low-income population. Cheap and efficient AI systems are more profitable than the real people, while the people working hard won’t bring the same benefit to the company.
The covid-19 pandemic brings everyone online, and e-commerce is rising rapidly as well as influencers. An AI influencer Lil Miquela is, currently with 3 million followers on Instagram. In 2018, Lil was employed as a contributing art editor for the Dazed online magazine website. Art editor in a fashion company is an important role, and people who work for the company could wait for the position. The happening Lil Miquela is an AI robot for fashion brand promotion and the algorithms of desire (Lacković, 2021). The company uses Algorithmalgorithm produce a popular avatar that fits with the market. The way Lil Miquela presents on Instagram is the same as a normal influencer, and it blurs the gap between virtual and real. When an AI replaced human really happens, issues and comments are put forward to the public:
(@its1833, 2018)
Ethical issues in Society
Racial and Gender Bias
There are increasing cases of racism and sexism on news and social media platforms. As I discussed that the individual can get a broader view when they’re in a community relation, some of the algorithm decision-making processes also ignore social and human contexts, which matter to everyone who can engage with the AI systems (Noble, 2018). The Algorithmalgorithm is built on an enormous quantity of data, while we believe that the algorithmic calculation is neutral that could present a relatively equal online environment. Still, Noble introduced his finding from well-documented studies of Silicon Valley. People who developed the Algorithmalgorithm have various types of values and give enormous publicity to support racism, sexism, and false notions of meritocracy.
The issues of racial and gender bias never go away. On Weibo (a Chinese social discussion platform), interracial couple bloggers are popular and interesting to the audience. However, the situation changed when a Chinese girl found a black boyfriend. Towards the girl, the crowd started to discuss “Mei Wai” (try to flatter foreigners), have a negative attitude toward the black-color baby, and question the dignity of the girl. While some other comments are talking about how black people have messy family relationships and sexual life and provide false information about the black people as Aids carriers, etc. People who have extreme racism and sexist comment are mostly male. How do they be related to the specific blog through the Algorithmalgorithm? Why so? These comments are harmful and destructive. The impact of the Algorithmalgorithm will lead to unknown consequences for the blogger. No matter whether the social reality is subjective, objective, or symbolic, reality interactions are happening in the digital space. To construct a better social order, internet culture needs governance to embed social values through preventing or providing social practice and activities (Nissenbaum, as cited in Just & Latzer, 2017).
Inequality
With more AI engaged in business modules, less laboratory the company needs, which means the company could have better productivity with fewer employees or job positions. It intensifies the wealth disparity that could bring terrible social issues such as an increased unemployment rate. The AI-driven industry could monopolies the market with the advantage of the cheap and efficient characteristics of AI labor. Eventually, other middle and small-sized enterprises will close, and the AI-driven company will occupy the huge opportunity and centralize the economic power within a few people.
Despite the economic inequality, there’s also information inequality between AI-driven companies such as Google and common users. Different forms of online participation provide data to these companies, while the automatically algorithmic selection controls users what information they should see, and AI always helps the company to encourage users’ consumption behaviors. Personalized information strengthens individualization and threads social cohesion and order (Schroer, as cited in Just & Latzer, 2017).
The big data set is always under the control of social-media companies. Digital inequality always happens when users need to use the online platform and signup for the agreement without really looking at the content. ZAO, a Chinese AI face-swapping application using the public’s digital inequality situation on stealing personal data and making it legal in the app agreement. Eventually, these action is exposed by a lawyer user and get punished. Lacković(2021) warns users that they need to consider the functionality of images and increase the need for critical media literacy in the post-digital age.
Transparency
Just and Latzer(2017) introduce the Algorithmic reality construction that includes the private sector like social-media companies and interests, a new social trend of platformization, and algorithms as main actors and policy-makers. While these actors also determined the criteria for the socially sensitive selection process though commonly using the personalized software and services. Compared to pubic algorithm governance, private governance dominates the algorithmic-driven market. When both public and private algorithmic services are able to help governance, a new challenge called “black box” is raised, and it refers to proprietary private data sets with secret throughput stage and algorithms while its algorithmic reality production behavior is noticeable. Low levels of transparency limit the application of the accountability system. (Diakopoulos, as cited in Just & Latzer, 2017)
Face ID provided by Apple company is a great example of the “black box” challenge. iPhone users could easily tell the unlocking process by watching the animation on the screen. They can easily follow the set-up instruction, using the Face ID for daily purchases or just to unlock the screen. Yet the underlying machine learning process is still opaque.
Algorithmic selection is mainly used for commercial purposes instead of political goals, and it delivers and highlight commercialization.
Conclusion
This blog discusses potential or current ethical issues on Artificial Intelligence and relates internet governance with algorithm reality construction culture in two aspects. On the one hand, AI has a strong impact on shaping individuals’ behaviors also the way we perceive the world. And together, Automated algorithmic selection applications will help shape the construction of an individual’s reality. Algorithmic reality construction could lead to individualization and has a negative impact on social cohesion. The dominance of private algorithmic governance in the market is primarily aiming for commercial goals. Yet they ignore the social responsibility and increase social inequality. All in all, the exploration of Internet governance uses algorithmic selection to construct an individual’s reality and then reshape the social order. Although there’re challenges, the construction of ethical algorithmic governance will keep working and looking for a better AI culture environment.
Reference List:
Article
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Byford, S. (2017, May 23). Google’s AlphaGo AI defeats world Go number one Ke Jie. The Verge. https://www.theverge.com/2017/5/23/15679110/go-alphago-ke-jie-match-google-deepmind-ai-2017
Crawford, K. (2021). The atlas of AI power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press.
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts. The Journal of Artificial Intelligence Research, 62, 729–754. https://doi.org/10.1613/jair.1.11222
Lacković, N. (2021). Postdigital Living and Algorithms of Desire. Postdigital Science and Education, 3(2), 280–282. https://doi.org/10.1007/s42438-020-00141-4
Just, N., & Latzer, M. (2017). Governance by algorithms: reality construction by algorithmic selection on the Internet. Media, Culture & Society, 39(2), 238–258. https://doi.org/10.1177/0163443716643157
Noble, S. U. (2018). Algorithms of oppression : how search engines reinforce racism. New York: New York University Press.
Pasquale, F. (2015). The black box society : the secret algorithms that control money and information. Cambridge: Harvard University Press.
@its1833. (2018, October 17). https://twitter.com/its1833/status/1052186504913723392. Twitter. https://twitter.com/its1833/status/1052186504913723392?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1052186504913723392%7Ctwgr%5E%7Ctwcon%5Es1_&ref_url=https%3A%2F%2Fcms.qz.com%2Fembed-sandbox%2F1425735%2Ftwitter.com%2Fits1833%2Fstatus%2F1052186504913723392
Image
Tillman, M. (2022). What is Apple Face ID and how does it work? In pocket-lint. https://www.pocket-lint.com/phones/news/apple/142207-what-is-apple-face-id-and-how-does-it-work