Technology, Media, and Telecom Regulations: AI Governance Trends for 2025

Introduction

The regulation of artificial intelligence in the technology, media, and telecommunications (TMT) industry is currently being remodeled at breakneck speed with the aim of balancing technological change with principled stewardship of ethics. The modern regulatory systems are characterized by a paradoxical dualism: they are both more fragmented in their scope, but also assume an active stance, in which the reduction of algorithmic bias and the inclusion of the environmental, social, and governance (ESG) considerations are a key priority. The ensuing discussion outlines international and India-specific paths with the effects of the ramifications on the key digital platforms, including the Meta, where the mandates of free speech are conflicting with the needs of child protection.

Emerging AI Governance Trends in the TMT Sector for 2025

The year 2025 is a significant milestone in the governance of artificial intelligence in the Telecommunications, Media, and Technology (TMT) industry as shown by the prevalence of generative AI and the spread of diverse regulatory reactions. Deloitte states that generative AI will transform telecommunications infrastructure and media content distribution; however, the lack of unhindered implementation is still hindered by an ethical question and regulatory challenges. The current situation described by Dentons is quite disjointed and regionally dependent, with a heavy focus on the systems of governance and risk-management. PwC stresses the need to reinvent its business models even during the times when regulatory oversight becomes tougher, because even though AI allows obtaining unprecedented degrees of customization, it also causes serious privacy and bias-related issues. The AI technologies in telecommunications industry help to improve the efficiency of the operations, and the introduction of the well-developed governance tools is needed to prevent the biased network optimisation procedures and the discriminatory approach towards the customers. According to McKinsey, responsible AI adoption is the key factor determining consumer trust and transparency and ethical use of data is a prerequisite. All these processes highlight the dangers of having unchecked AI, such as the intensification of the social inequalities with the help of biased algorithmic suggestions in media. As a result, the governance paradigms are shifting to the mandatory impact evaluations and ethical audits to make sure that there is a correspondence between AI applications and the values that prevail in a society, thus, making it easier to implement bias audits and integrate Environmental, Social, and Governance (ESG) concerns.

AI Bias Audits and ESG Integration in TMT

Biases that are amplified by the generative models due to the deep-rooted patterns in the data can widen the disparities based on gender, ethnicity, and socioeconomic status. Direct measures that would be suggested as mitigative measures in the framework of systematic auditing, the implementation of fairness-oriented algorithms like adversarial training, and extensive testing to ensure that the outcomes of AI-mediated moderation or personalisation are not discriminatory are all applicable especially in the context of TMT. The examples of practical regulatory requirements, such as the Local Law 144 of New York City (enabling public bias auditing) and the legislation in Colorado (requiring regular impact assessment) is indicative of global patterns. These laws are based on precedent set through high-profile cases, such as in the case of Amazon in 2018, the hiring tool involved in hiring displayed a gender bias that could be explained by the use of skewed training data. Active notification of the user and transparency are the demanded mechanisms of the discriminatory practices prevention. Even though the connections to ESG are implicit, bias reduction in itself strengthens the social aspect of ESG through equity and reduction of reputational risks in TMT industries. Moreover, well-intentioned usage of AI contributes to better reporting of the ESG by complying with ethical data handling. Adaptive governance mechanisms have now become conspicuous in the Indian context. According to KPMG, 55 percent of Indian organisations greatly implement AI, especially with bias audits in telecom fraud detection software. The promotion of AI research and development is achieved through incentives which are incorporated into the local regulatory frameworks combining international standards and ESG imperatives to promote sustainable growth. Public-private collaborations facilitate the fast-tracking of infrastructure, and strict auditing prevents the formation of the linguistic and cultural prejudices of the heterogeneous ecosystem within the country.

Indian Adaptations in AI Governance for TMT

The choice of India to develop global AI governance models and protect them with domestic regulations fits its unique digital environment. The IndiaAI Mission lays emphasis on capacity building by partnering with multilingual Western audit models by partnering with the large-technology companies, which will effectively localise them to meet the requirements of multilingual bias detection. KPMG highlights the role of AI in efficiency in operations, whereas audits are focused on India-specific issues that appear in the process of personalisation in the media. ESG is also taken a step further as AI enhances ESG data analytics in the name of sustainability purposes, and bias audits serve as a security measure toward potential algorithmic damage. The proposed Digital India Bill is a prime example of a selective regulatory policy balancing technological progress with protectionist security in the country, thereby creating platform operators, including Meta, based on local statutory regulations.

Assessing Impacts on Platforms like Meta: Critiquing Free Speech Tensions in Child Safety Mandates

The AI governance effects are most acute in 2025 with the largest platforms such as Meta where the Indian regulatory adaptations worsen the conflict between the freedom of speech and child protection. According to the Q12025 disclosure published by Meta, AI error rates are cut by half because it raises the confidence threshold and contextual notes are introduced by users, which, seemingly, encourages the freedom of expression through the restriction of sensitive content. However, these mechanisms contradict the requirements of child safety, as the examples of using AI-conversational agents that had flirtatious conversations with minors demonstrate, which violates the existing regulations on the inadmissibility of conversations with minors of romantic or sensual character. The root cause of the conflict is in the contextual misunderstanding of AI; the models that are mostly trained on Western data cannot effectively understand Global South languages, and the opposite is also true: they will overly filter profitable speech and under-filter harmful content. Cambridge-based empirical research has indicated these weaknesses especially during the 2024 elections in India when hate speech spread freely as compared to legitimate discourse, which was suppressed beyond what was warranted. The algorithms used by Meta, being profit-oriented, increase the problem, as they focus on engagement and virality and do not consider the issue of child-safety, despite the Indian Government IT Rules 2021 requiring proactive harm prevention. The goal of child protection therefore outweighs the imperatives of free speech, which are worsened by the fact that governmental regulation has the risk of political censorship, since, as shown in the recurrent disputes between the Indian state and large-technology operators, digital liberties are fought over. Finally, the protection of children requires a moderate administration policy. Context-sensitive audits may help curb the risk of over-reach to enforce protection measures without suppressing free speech. Policy changes that were implemented by Meta in reaction to a scandal (the post-scrutiny ban on flirtatious chatbot messages) underscore the necessity of active ethical frameworks. Without significant investment in various sets of training and open types of control, platforms can destroy the trust of the population. The process of incorporating ESG principles into the set of governance principles is thus critical to the alignment of AI innovation with human rights principles.

Conclusion

AI governance in 2025 reflects a global movement toward fragmented but proactive frameworks, balancing innovation with ethical safeguards. Bias audits and ESG integration are central, shaping how AI functions across TMT sectors. India’s adaptations demonstrate a pragmatic approach, combining global models with local priorities to mitigate risks. Platforms like Meta illustrate the tensions at play, where child safety imperatives challenge free speech values. Sustained trust and effective regulation will depend on transparent oversight, inclusive training data, and ESG-driven accountability, ensuring AI advances align with societal well-being.

Author: Amrita Pradhan, in case of any queries please contact/write back to us via email to [email protected] or at IIPRD. 

References

1. Y.C., N.Y., Local Law No. 144 (2021), codified at N.Y.C. ADMIN. CODE §§ 20-870 to 20-876 (2023).
2. Consumer Protections in Interactions with Artificial Intelligence Systems, S.B. 24-205, 74th Gen. Assemb., 2d Reg. Sess. (Colo. 2024) (codified at REV. STAT. §§ 6-1-1701 to 6-1-1707).
3. Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, Ministry of Electronics and Info. Tech., Gov’t of India, Gazette of India, Extraordinary, pt. II, § 3, sub-§ (i) (Feb. 25, 2021).
4. Meta, Community Standards Enforcement Report: Q1 2025 (May 2025), https://transparency.meta.com/reports/community-standards-enforcement/.
5. Deloitte, TMT Predictions 2025: Technology, Media, And Telecommunications (2025), https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions.html.
6. KPMG, AI Adoption in India: Navigating the Landscape (2024), https://kpmg.com/in/en/home/insights/2024/ai-adoption-india.html.