Motivation & Overview

India’s services sector is internationally renowned and has helped propel the country’s economic growth. Indeed, in recent years, a majority of the value added to India’s GDP has been concentrated in services. Especially noteworthy are India’s software and computing services, which include large multinational conglomerates like Infosys and Tata Communications Services. 

Yet as Indian software has flourished, the growth of its computer hardware and manufacturing has been sluggish. Tellingly, India is still a net importer of hardware and other electronics. At first glance, this divergence is puzzling because both the software and hardware sectors should have benefited from India’s educated labor pool and infrastructure. How can these different sectoral outcomes be explained?
 

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Fig. 1: Electronics production value compared to software and software service revenues.
 

In “Comparing Advantages in India’s Computer Hardware and Software Sectors,” Dinsha Mistree and Rehana Mohammed offer an explanation in terms of state capacity to meet the different functional needs of each sector. Their account of India’s computing history emphasizes the inability of various state ministries and agencies to agree on policies that would benefit the hardware sector, such as tariffs. Meanwhile, cumbersome rulemaking procedures inherited from British colonialism impeded the state’s flexibility. Although this disadvantaged India’s hardware sector, its software sector needed comparatively less from the state, building instead on international networks and the efforts of individual agencies.

The authors provide a historically and theoretically rich account of the political forces shaping India’s economic rise. The paper not only compares distinct moments in Indian history but also draws parallels with other landmark cases, like South Korea’s 1980s industrial surge. Such a sector-based analysis could be fruitfully applied to understand why different industries succeed or lag in emerging economies. 

Different Sectors, Different Needs

In order to become competitive — both domestically and (especially) internationally — hardware manufacturers often need much from the state, what the authors call a “produce and protect regime.” This can include the construction of factories and the formation of state-owned industries (SOEs), as well as tariffs to reduce competition or labor laws that restrict union strikes. Perhaps most importantly, manufacturers need a state whose legislators and bureaucrats can coordinate with each other in response to market challenges. Such a regime is incompatible with excessive “red tape” or with the “capture” of regulators by narrow interest groups. Because customers tend to view manufactured goods as “substitutable” with each other, firms will face intense competition as regards price and quality.
 

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Fig. 2: Inter-agency coordination required for sectoral success.
 

The situation is very different for service providers, whose success depends on building strong relationships with customers. States are not essential to this process, even if their promotional efforts can be helpful. Coordination across government agencies is similarly less important, as just one agency could provide tax breaks or host promotional events that benefit service providers. Compared with manufacturing, customers tend to view services as less substitutable — they are more intangible and customizable, which renders competition less fierce. Understanding India’s computing history reveals that the state’s inability to meet hardware manufacturers’ needs severely constrained the sector’s growth. 

The History of Indian Computing

Although India inherited a convoluted bureaucracy from the British Raj, the future of its computing industry in the 1960s seemed promising: political elites in New Delhi supported a produce-and-protect regime, relevant agencies and SOEs were created, and foreign computing firms like IBM successfully operated in the country. 

Yet by the 1970s, some bureaucrats and union leaders feared that automation would threaten the federal government’s functioning and India’s employment levels, respectively. Strict controls in both the public and private sectors were thus adopted, for example, requiring trade unions — which took a strong anti-computer stance — to approve the introduction of computers in specific industries. The authors make special mention of India’s semiconductor industry. It arguably failed to develop due to lackluster government investment, the need for manufacturers to obtain multiple permits across agencies, decision makers ignoring recommendations from specialized panels, and so on.

Meanwhile, implementing protectionist policies proved challenging. For example, decisions to allow the importation of previously banned components required permission from multiple ministries and agencies. After India’s 1970s balance-of-payments crisis, international companies deemed inessential were forced to dilute their equity to 40% and take on an Indian partner. IBM then left the Indian market. At the same time, SOEs faced growing competition over government contracts and workers, owing to the growth of state-level SOEs.

The mid-1980s represented a partial turning point as Rajiv Gandhi became Prime Minister and liberalized the computing industry. Within weeks, Rajiv introduced a host of new policies and shifted the government’s focus from supporting public sector production to promoting private firms, which would no longer face manufacturing limits and would be eligible for duty exemptions. Changes to tariff rates and import limits would not require approval from multiple agencies. Meanwhile, international firms reengaged with Indian markets via the building of satellite links, facilitating cross-continental work, such as between Citibank employees in Mumbai and Santa Cruz.

However, this liberalizing period was undermined and partially reversed after 1989, when Rajiv’s Congress Party (INC) lost its legislative majority and public policy became considerably more fragmented. Anti-computerization forces, especially the powerful Indian trade unions, worked to stymie Rajiv’s reforms. Pro-market reformists were forced out of their positions in Indian bureaucracies. Rajiv was assassinated in 1991, after which Congress formed a minority government with computer advocate P. V. Narasimha Rao as PM. Yet all of this occurred at a delicate time, as India was at risk of defaulting and had almost completely exhausted its foreign exchange.

By the late 1990s, both the hardware and software sectors should have benefited from the rising global demand for computers, yet India’s history of poor state coordination hindered manufacturers. Meanwhile, software firms were able to take advantage of global opportunities given their comparatively limited needs from state actors and political networks — for example, helping European Union banks change their computer systems to Euros. Ultimately, the Indian state has powerfully shaped the fortunes of these different sectors.

*Research-in-Brief prepared by Adam Fefer.

Introduction

Generative AI has become an incredibly attractive and widespread tool for people across the world. Alongside its rapid growth, AI tools present a host of ethical challenges relating to consent, security, and privacy, among others. As Generative AI has been spearheaded primarily by large technology companies, these ethical challenges — especially as viewed from the vantage point of ordinary people — risk being overlooked for the sake of market competition and profit. What is needed, therefore, is a deeper understanding of and attention to how ordinary people perceive AI, including its costs and benefits.

The Meta Community Forum Results Analysis, authored by Samuel Chang, James S. Fishkin, Ricky Hernandez Marquez, Ayushi Kadakia, Alice Siu, and Robert Taylor, aims to address some of these challenges. A partnership between CDDRL’s Deliberative Democracy Lab and Meta, the forum enables participants to learn about and collectively reflect on AI. The impulse behind deliberative democracy is straightforward: people affected by some policy or program should have the right to communicate about its contents and to understand the reasons for its adoption. As Generative AI and the companies that produce it become increasingly powerful, democratic input becomes even more essential to ensure their accountability. 

Motivation & Takeaways

In October 2024, the third Meta Community Forum took place. Its importance derives from the advancements in Generative AI since October 2023, when the last round of deliberations was held. One such advancement is the move beyond AI chatbots to AI agents, which can solve more complex tasks and adapt in real-time to improve responses. A second advancement is that AI has become multimodal, moving beyond the generation of text and into images, video, and audio. These advancements raise new questions and challenges. As such, the third forum provided participants with the opportunity to deliberate on a range of policy proposals, organized around two key themes: how AI agents should interact with users and how they should provide proactive and personalized experiences for them.

To summarize some of the forum’s core findings: the majority of participants value transparency and consent in their interactions with AI agents as well as the security and privacy of their data. In turn, they are less comfortable with agents autonomously completing tasks if this is not transparent to them. Participants have a positive outlook on AI agents but want to have control over their interactions. Regarding the deliberations themselves, participants rated the forum highly and felt that it exposed them to alternative perspectives. The deliberators wanted to learn more about AI for themselves, which was evidenced by their increased use of these tools after the deliberations. Future reports will explore the reasoning and arguments that they used while deliberating.
 

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^{The participants of this Community Forum were representative samples of the general population from five countries - Turkey, Saudi Arabia, India, Nigeria, and South Africa. Participants from each country deliberated separately in English, Hindi, Turkish, or Arabic.}

Methodology & Data

The deliberations involved around 900 participants from five countries: India, Nigeria, Saudi Arabia, South Africa, and Turkey. Participants varied in terms of age, gender, education, and urbanicity. Because the deliberative groups were recruited independently, the forum can be seen as five independent deliberations. Deliberations alternated between small group discussions and ‘plenary sessions,’ where experts answered questions drawn from the small groups. There were around 1000 participants in the control group, who did pre- and post-surveys, but without deliberating. The participant sample was representative with respect to gender, while the treatment and control groups were balanced on demography as well as on their attitudes toward AI. Before deliberating on the proposals, participants were presented with background materials as well as a list of costs and benefits to consider.

In terms of the survey data, large majorities of participants had previously used AI. There was a statistically significant increase in these proportions after the forum. For example, in Turkey, usage rates increased from nearly 70% to 84%. In several countries, there were large increases in participants’ sense of AI’s positive benefits after deliberating, as well as a statistically significant increase in their interest. The deliberations changed participants’ opinions about a host of claims; for example, “people will feel less lonely with AI” and “more proactive [agents] are intrusive” lost approval whereas “AI agents’ capability to increase efficiency…is saving many companies a lot of time and resources” and “AI agents are helping people become more creative” gained approval. After deliberating, participants demonstrated an improved understanding of some factual aspects of AI, although the more technical aspects of this remain challenging. One example here is AI hallucinations, or rather, the generation of false or nonsensical outputs, usually because of flawed training data.
 

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Proposals

Participants deliberated on nineteen policy proposals. To summarize these briefly: In terms of whether and how AI remembers users’ past behaviors and preferences, participants preferred proposals that allowed users to make active choices, as opposed to this being a default setting or only being asked once. They also preferred being reminded about the ability of AI agents to personalize their experience, as well as agents being transparent with users about the tasks they complete. Participants preferred that users be educated on AI before using it, as well as being informed when AI is picking up on certain emotional cues and responding in “human-like” ways. They also preferred proposals whereby AI would ask clarifying questions before generating output. Finally, when it comes to agents helping users with real-life relationships, this was seen as more permissible when the other person was informed. Across the proposals, gender was neither a significant nor consistent determinant of how they were rated. Ultimately, the Meta Community Forum offers a model for how informed, public communication can shape AI and the ethical challenges it raises.