Conceptual map for artificial intelligence policy discourse analysis

The key concepts delivered by the text include “education for more AI talents,” “education on AI,” “retraining program,” “creating new job,” and “attracting global talents.” Although these concepts seen unrelated, they can be situated in the conceptual map. Amongst them, “education for more AI talents,” and “attracting global talents” can be situated under “talent policy” which belongs to “industrial policy,” and together with “creating new job,” they belong to “AI-related benefits.” “Retraining program,” and “education on AI,” can be situated under “labor shift” which belongs to “AI-related risks.”

Two hypotheses can be drawn from the discourse analysis above by using the conceptual map:

1. The UK has a talent policy for cultivating AI-related benefits

2. The UK tries to prepare for the labor shift for minimizing AI-related risks

Returning to the text, more evidence of the UK talent policy can be found. For instance:

“A key ambition of the industrial strategy is for the UK to be the world's most innovative economy - this Sector Deal aims to attract and retain both domestic and global AI talent.”

“Doubled Tier 1 (Exceptional Talent) visas to 2,000 a year to attract the best and brightest talent in science, digital technology (including specialists in AI), engineering, arts and creative sectors.”

On the other hand, the data does not indicate that there is the absence of discourse about preparations for labor shift. Thus, the first hypothesis may be considered accurate whereas the second hypothsis is considered false until further evidence. Moreover, the absence of discourse also suggests that the dominant discourse reflected in the text is AI-related benefits which can be treated as a hypothesis that can be used for a more comprehensive discourse analysis.

Summary of Findings

First and foremost, the most important finding of this paper is that it provides a conceptual map that can be utilized for conducting discourse analyses on AI policy. It attempts to provide a conceptual framework for understanding the implications of AI policy that diffuse into other areas. It enhances the connectivity between the various aspects of AI policy which exhibits an interdisciplinary nature. It also attempts to bridge the theoretical gaps between different branches of AI policy. Some important concepts are explicated in group for demonstrating the connections between these concepts. Secondly, the dominant set of discourses is identified. AI-related risks and AI-related benefits serve as the main theme that is shared by four distinctively different countries - China, US, Germany, and India. Thirdly, the author demonstrates two cases where the conceptual map is utilized in discourse analyses for gaining valuable insights.

Discussion

Regarding the first research question - how can we interpret an AI policy discourse? - the author concludes the underlying messages, meanings, focus, and fields of actions become more vivid with the help of conceptual map. Let us review the text taken from the CBInsights reports.

“The Chinese government is promoting a futuristic artificial intelligence plan that encompasses everything from smart agriculture and intelligent logistics to military applications and new employment opportunities growing out of AI.”

Guided by the map, now the author is able to reveal more meanings and contexts in this paragraph by situating these key words and examining their relationships with other concepts. For instance, “smart agriculture” and “intelligent logistics” are sectors where AI can potential promote growth. They suggest economic benefits related to AI must be an apparent discourse in Chinese AI plan. Military applications of AI can strengthen national security and, sometimes, increase security vulnerability. Moreover, there are concerns of autonomous weapons and their consequential risks. AI, just like other new technologies, can create new jobs; however, whether or not China is preparing for the negative impacts of AI on workforce and job market may be something interesting to discover. Despite the fact that important conceptual insights can be gained from the use of the conceptual map, , the author does not seek to claim that the conceptual map exhibits theoretical relevance beyond the extend of discourse analysis, since a unified theory for AI policy is far from being conceivable.

Another interesting “convergence point” where several concepts merge into one is the concept of “soft power.” Its connections with other concepts are demonstrated by the fraction of conceptual map below.

During coding process, the author identifies that governments are actively trying to promote their own values in AI policy. For instance, in “Three-Year Action Plan for Promoting Development of a New Generation Artificial Intelligence Industry (2018-2020),” “Xi Jingping Thought” is considered the guiding ideology. More importantly, a connection between the theories of Xi Jingping thought and AI policy is evident. For instance, the document says that

“In accordance with the “five in one” overall layout and the “four comprehensives” strategic layout, diligently promote the Party Central Committee and State Council's policy directive to focus on deep integration of information technology and manufacturing technology.”

“Five in one (ÎåλһÌå),” and “four comprehensive (ËĸöÈ«Ãæ)” are concepts that are first proposed during the 18th National Congress of the Communist Party of China in 2012 when Xi Jinping first assumed office. In the 19th National Congress, they are added to the constitution of the Communist Party of China. The ultimate objective for implementing “five in one,” and “four comprehensives” is for building a “moderately prosperous society.” Similarly, in other documents, the practices of promoting values are also vivid, but the implications of values on AI is sometimes difficult to imagine. Particularly with the case of China, what can Xi Jinping Thought on AI policy be about?

The other feather that these documents share is that they seek to “define” their position in the age of AI. For instance, the US seeks to “maintain its global leadership,” Germany hopes to achieve and maintain “leading global excellence in the research and development,” and India aspires to “position itself among leaders on the global AI map.”

During coding, the author finds it difficult to draw connections for these two codes without over-conceptualizing or being presumptuous. What provides an inspiration is the concept of “soft power,” purposed by Joseph Nye. He argues that the soft power of a country relies on three things - culture, political values, and foreign policies Nye, Joseph S. The Future of Power. 2011. Public Affairs. . Thus, the author decides to put them under the same unifying code - soft power.

In the work of Elaine Kamarck titled “Malevolent soft power, AI, and the threat to democracy, she argues that the use of cyberattack on a foreign country and ultimately influence its election can be considered a form of soft power. Can this be considered “soft power?” or is it a complete different kind of “cyber power?” The author remains critical towards Elaine Kamarck's argument. Based on the conceptual map developed by the author and following analyses of the general corpus, no evidence can be found that may indicate the connection between cyberattack and soft power.

From a discourse analysis approach, two discourses - governments' emphases on “values” and “leadership,” - are apparent in at least one document. These discourses are typically placed in the introduction or an executive summary signifying their importance. Thus, in contrast to Elaine, the author believes that AI soft power consists of two elements - values and leaderships. It also represents a higher conceptual proximity to the definition given by Nye.

Another potential use of developing hypothesis is by looking at discourse changes overtime. In the case of US AI policy, two sets of documents are released with a two-year gap. The first set of reports published in 2016, has larger volumes and touches on more aspects of AI and AI policy; however, the report that was released in 2018 has a significantly smaller volume, and many discourses that previously existed in the former set of reports seize to exist. What are these disappeared discourses? What are the relations between these discourses? By operationalizing the conceptual map many hypotheses can be generated and further used by discourse analysts.

In a world where the development of AI enters a new era, an ideal conceptual map for AI policy, in the opinion of the author, should be dynamic and transcend. It should be one that can, on one hand, capture the developments of AI and their impacts on all levels, and on the other hand, maintains a traceable structure. For bringing a conceptual map closer to the idea model, it requires frequent updates, or it may become significantly outdated within a short period of time. More importantly, this conceptual map is far from mature from a cultural point of view. The data that the author uses in grounded theory analysis includes documents from China, US, Germany, India, and France. These countries can be flexibly clustered into various groups such as “major economies v. medium-sized economies,” “developed world v. emerging markets,” and “western civilization v. Asian civilization (s);” however, as more developing countries such as Kenya are becoming increasingly aware of the potentials of AI, more data needs to be included for better capturing the diversity of approaches in areas such as values, unemployment, and equality.

The possible presence of an AI-related neutrality is another interesting area of discussions. On one hand, a weak AI-related neutrality discourse, or the absence of which, reflects to the great uncertainties regarding AI. Theories are grounded on the reality, or sometimes the perceived reality. When the reality becomes uncertain, so does theories relate to it. For closing the discussion, the author selected a line from “Rise of Machine,” because of how it accurately encapsulates such uncertainties.

“…the workforce is affected by AI; whether that effect is positive, negative, or neutral remains to be seen.”

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