On The Media-Political Dimension Of Artificial Intelligence

Sudmann, Andreas

doi:10.14361/dcs-2018-040110

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such risks go beyond loss of jobs; in addition to economic challenges, Dickson (2017) identifies ethical challenges such as dealing with machine bias, legal challenges such as assigning blame when an AI makes a mistake, and privacy challenges such as how to properly manage the acquisition of data necessary for machine learning. AI learning is only as good as the data that underlies it, and it can often be difficult to detect when a problem exists with an AI's learning algorithm because of how hard those behaviors are to parse (Dickson 2018;Sudmann 2018). Even more insidiously, malicious actors could manipulate AI decision-making by intentionally corrupting the data they use to learn.…”

Section: The Relevance Of Games For Aimentioning

confidence: 99%

Alive: A Case Study of the Design of an AI Conversation Simulator

Walsh

2020

EJCGC

View full text Add to dashboard Cite

This article offers a case study of a game designed to encourage its players to reflect on the implications of Artificial Intelligence (AI) by considering the impact that advancements in AI might have on them personally or on the world more broadly. Alive is a conversation simulator in the vein of the Lifeline series of games, where the player responds to messages from a newly created AI in a manner simulating the rhythm of a conversation vis phone texts. Player decisions directly influence how the AI develops over time and the values it chooses to adopt. Throughout the narrative, the game explores a variety of topics relevant to the creation of AI, such as the potential differences between how an AI and a human would view the world, the capacity of an AI to evolve or change over time, and the risks inherent in the creation of a self-aware AI. In this article, I describe the development of a working prototype of my game, made freely available to accompany this piece. After first establishing the basic principles of conversation simulators based on an analysis of existing examples, I chronicle the design decisions I made and offer my rationale for them. I also discuss the difficulties I encountered in covering this topic and propose what I see as helpful design takeaways for creating other games in a similar vein. It is my hope that this article provides practical tools to scholars and designers interested in both creating and interrogating complex topics such as AI through games.

show abstract

Section: The Relevance Of Games For Aimentioning

confidence: 99%

Alive: A Case Study of the Design of an AI Conversation Simulator

Walsh

2020

EJCGC

View full text Add to dashboard Cite

show abstract

“…These issues arise from AI being generally opaque (ibid. ), even if it is possible to develop applications to observe AI learning processes (Sudmann 2018a). Also, the envisioned ubiquity of AI applications elicits broad discussions of the consequences for labor cultures (AI for optimizing work processes and automation) and social environments (sensor-based observation systems).…”

Section: Conditions Of Appearance: Visibility and Invisibilitymentioning

confidence: 99%

Artificial Intelligence

Förster¹

2019

The Democratization of Artificial Intelligence

View full text Add to dashboard Cite

“…While mostly in line with Pasquale's effort to decipher the opaqueness of our data-driven world, it also appears significant to question the limits of the black box as a heuristic if not holistic image. Scholars such as Geiger (2017), Sudmann (2018), Burrell (2016) or Bucher (2016) have explored this territory. The latter, for instance, has argued that "the widespread notion of algorithms as black boxes may prevent research more than encouraging it", noting that the notion is "too readily used" (84).…”

Section: Introductionmentioning

confidence: 99%

“…2018). Whereas the first relied on deduction, explicit modeling, abstract rules and programmable languages to create a logical and formal mode of reasoning, the second is based on induction, whereby connected hypotheses and approximations produce "optimized" perceptions and predictions about what is going on in the data, inasmuch as data translates into improved rates of predictability (Mackenzie 2017, Sudmann 2018. Layers of non-linear calculus thus inform something of a "deep" but shallow architecture which does not necessarily form an inexplicable AI, but which pushes the limits of its explicability further away.…”

Section: Introductionmentioning

confidence: 99%