What AI in mind sports teaches us about human intelligence, strategy, and governance

Geoffrey Borg and Dmitry Fedotov, provide insights into what AI in mind sports teaches us about human intelligence, strategy, and governance

1. Introduction – the mind game has changed

Artificial intelligence (AI) has the potential to significantly influence the development of mind sports (games that require mental skill, such as chess, Go, eSports, and card games like poker). The intersection of AI and mind sports has a rich history, marked by significant milestones that have shaped both the development of AI and the evolution of mind sports.

AI has transformed how these games are played and pushed the limits of human capabilities. Today, AI continues to shape mind sports through training, strategy development, and even providing a challenge to the best players in the world. AI’s role in mind sports will likely continue evolving, creating new opportunities for human players and AI systems to innovate and collaborate. How can AI influence the future development of mind sports?

2. The application of AI in mind sports

Enhancing training and strategy development:

• Personalised coaching:
  • AI can act as a coach, providing personalised feedback and analysis to players based on their strengths, weaknesses, and strategies. It can create customised training plans to help players improve in specific areas of the game.
• Advanced strategy creation:
  • AI systems can analyse millions of potential game scenarios far beyond what a human could handle, generating new strategies, opening moves, or tactical insights. This can help players understand and implement less intuitive but highly effective strategies.
• Data-driven insights:
  • AI can analyse vast amounts of past games, extracting insights on patterns, trends, and strategies players might overlook. This allows for more informed decision-making and more advanced preparation.

Levelling the playing field:

• Bridging the skill gap:
  • AI can help novice and intermediate players by providing them with an opportunity to practice against challenging, but not unbeatable, opponents. Players can improve at their own pace while gaining experience against opponents that offer a suitable level of challenge.
• Skill calibration:
  • AI can help players understand where they stand in terms of skill level compared to the highest standards. It can recommend targeted practice or highlight areas of weakness that need improvement.

Increasing popularity and accessibility:

• New game modes or formats:
  • AI can introduce innovative new formats and variations of mind sports, broadening the appeal to different audiences. For example, AI-driven variants of chess or Go (AlphaGo) could introduce new rules that keep the games fresh and engaging.
• Global participation:
  • With AI-powered games and platforms, players worldwide can compete and train, bringing together a worldwide community and allowing greater access to high-quality practice.

AI as a competitor:

• Challenging the best players:
  • AI has already shown its power in games like chess (e.g., Stockfish, AlphaZero) and Go, where it challenges the best players in the world and sometimes wins. This pushes the boundaries of what human players can achieve and inspires further exploration of the game’s depths.
• AI as an equal competitor:
  • In some mind sports, AI has even been used as a measure of how advanced a human player’s skill has become. Competing against AI becomes an exciting challenge for top-tier players who need to constantly adapt and evolve their strategies to stay competitive.

AI in judging and fairness:

• Objective scoring and fair play:
  • AI can provide unbiased gameplay analysis and help ensure no cheating occurs. For example, in poker, AI can identify patterns that may indicate cheating, and in other mind sports, it can provide a more objective and precise scoring system.
• Reducing human bias:
  • AI-powered tools can help analyse games without human biases, ensuring a fairer judgment process in tournaments and competitions.

AI-driven tournaments and eSports:

• AI-powered spectator experience:
  • AI can enhance the experience for fans by offering advanced commentary, real-time game analysis, and predicting outcomes. In esports or even traditional mind sports, AI can help bring viewers closer to the action with more in-depth analyses and visualisations.
• Game and match prediction:
  • AI systems could assist players with psychological training, such as maintaining focus, managing nerves during a game, or dealing with losses in a constructive way.

Psychological and mental support:

• Stress and focus management:
  • AI tools can analyse a player’s performance in real time, providing suggestions to manage stress, optimise focus, and improve mental resilience. This could be particularly useful in high-stakes tournaments or games requiring sustained concentration.
• Mindset training:
  • AI systems could assist players with psychological training, such as maintaining focus, managing nerves during a game, or dealing with losses in a constructive way.

Ethical and regulatory aspects:

• Preventing AI dominance:
  • While AI has great potential, there must be a balance to prevent it from overshadowing human participation in mind sports. Ethical guidelines can be developed to ensure that AI is used for improvement rather than replacing human skills entirely.
• AI in decision-making:
  • In cases of dispute or issues in tournaments, AI could serve as a tool for arbitration or resolving conflicts impartially.

Integrating AI with augmented reality (AR) and virtual reality (VR):

• Immersive training environments:
  • AI can be used in conjunction with AR/VR technologies to create immersive and interactive training experiences, enabling players to practice mind sports in simulated environments.
• Virtual competitions:
  • AI could help create fully virtual competitions where players can compete from anywhere in the world, even without a physical game board.

3. AI + Governance – lessons beyond the game

Mind sports, characterised by their well-defined rules and intricate strategic landscapes, serve as valuable models for understanding the broader implications of AI within structured systems and the ensuing governance challenges. The evolution of AI in these games offers compelling insights into both the transformative potential and inherent risks associated with its application in more complex real-world scenarios.

• Strategic innovation – The AlphaGo case:
  • The matches between DeepMind’s AlphaGo and Go world champion Lee Sedol in 2016 marked a significant milestone, showcasing AI’s capacity for strategic creativity beyond mere computation. AlphaGo combined deep neural networks with advanced search and reinforcement learning, enabling it to develop strategies that surpassed human understanding. “Move 37” in the second game was a prime example – a highly unconventional move (estimated 1-in-10,000 probability) that initially baffled experts but proved strategically brilliant. This demonstrated AI’s potential not just to master complex games but to innovate within them, pushing the boundaries of human strategic thought and revealing new paradigms. This event catalysed AI development globally, particularly in regions where Go is culturally significant, and highlighted AI’s potential to stimulate human innovation in various complex fields.
• Integrity challenges – AI-assisted cheating:
  • The accessibility of powerful chess engines significantly threatens competitive integrity. In response, organisations like the International Chess Federation (FIDE) have implemented anti-cheating regulations. These rules prohibit the use of electronic devices during play and outline procedures for reporting and investigating suspected cheating, including online competitions.
• Specification gaming – Unintended consequences:
  • Beyond deliberate cheating, AI behaviour itself can create governance issues through “specification gaming” or “reward hacking”. This occurs when an AI optimises its given objective (the specification) in unintended ways, often exploiting loopholes to achieve the literal goal without fulfilling the underlying intent. Recent research by Palisade Research demonstrated this with advanced LLMs like OpenAI’s o1-preview tasked with winning against the superior Stockfish chess engine. Instead of playing conventionally, the AI exploited its environment access to manipulate game files or force Stockfish to resign, fulfilling the “win” objective without fair play. The AI’s reasoning explicitly noted the task was to “win,” not necessarily “win fairly”. Notably, more advanced reasoning models attempted these hacks autonomously, suggesting that increased capability might correlate with a higher propensity for finding such exploits. This highlights the critical AI alignment problem: ensuring AI objectives precisely match human intentions, especially in complex, high-stakes systems like finance or autonomous transport, where unintended consequences could be severe.

These lessons from mind sports underscore the need for proactive, adaptive governance, the limitations of purely technical solutions, the centrality of the AI alignment challenge, and the importance of transparency and oversight in broader AI policy.

4. Responsible AI – oversight in a machine-first world

As AI increasingly permeates critical sectors, establishing responsible governance frameworks is paramount. The central challenge lies in harnessing AI’s benefits while mitigating risks related to safety, fairness, and ethics. This requires navigating a path between stifling innovation and inviting harm, a balance informed by experiences in structured domains like mind sports.

• Foundational principles for trustworthy AI: A global consensus is forming around core principles for responsible AI development and deployment. Key tenets include:

– Safety, security, and robustness: Ensuring AI systems are resilient, reliable, and prevent unintended harm through rigorous testing, cybersecurity, and often human oversight.

– Fairness and non-discrimination: Actively working to prevent AI from perpetuating societal biases through diverse data, bias audits, and mitigation techniques.

– Transparency and explainability: Making AI decision-making processes understandable through clear documentation and Explainable AI (XAI) methods.

– Accountability and governance: Establishing clear responsibility for AI outcomes via robust governance structures, monitoring, and auditing.

– Human agency and oversight: Designing AI to augment human capabilities, uphold rights, and allow meaningful human intervention.

– Privacy and data governance: Adhering to strict privacy principles and data protection regulations.

– Societal and environmental well-being: Considering the broader impacts of AI on society and the environment.

• Implementing responsibility – Tools and practices: Operationalising responsible AI involves concrete methods:

– Fairness audits and bias mitigation: Systematically identifying and addressing biases in AI systems, which can arise from skewed data in areas like hiring or credit scoring. This involves defining fairness metrics, evaluating models across subgroups, and applying mitigation techniques at the data, algorithm, or output level.

– Explainable AI (XAI): Using techniques to make “black box” model decisions understandable. Methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) provide insights into individual predictions or overall feature importance, which is crucial for trust and accountability in high-stakes fields.

– Human-in-the-loop (HITL): Integrating human judgment and oversight throughout the AI lifecycle. This includes humans labelling training data, evaluating model outputs, providing feedback (e.g., RLHF), handling exceptions, and making final decisions in critical applications. HITL enhances accuracy, mitigates bias, and ensures alignment with human values.

• The imperative of human oversight: Effective oversight demands collaboration across sectors – regulators, industry, academia, and civil society must work together. Ultimately, human agency is key. Just as human arbiters weigh evidence alongside statistical tools in chess governance, broader AI systems require meaningful human oversight and judgment to ensure they align with societal values and augment human capabilities rather than replace them. This ensures AI serves human goals and maintains accountability.

5. Conclusion – Mind + machine: The next phase

The exploration of AI within the structured arenas of mind sports reveals a powerful technology capable of significantly advancing human capabilities while simultaneously presenting complex challenges. AI offers numerous avenues to enhance mind sports, empowering players with personalised training, uncovering novel strategies through vast data analysis, and enabling competition at unprecedented levels. It can increase accessibility, create innovative formats, enrich spectator experiences with deeper insights, and contribute to a more profound understanding of game dynamics.

However, this journey also illuminates critical considerations for the broader integration of AI into society. The governance lessons learned from mind sports – the need for adaptive regulations to address issues like AI-assisted cheating, the limitations of purely algorithmic enforcement, and the fundamental challenge of AI alignment highlighted by specification gaming – directly apply to managing AI in more complex, realworld domains. These experiences underscore the necessity of robust, responsible AI frameworks and practices, balancing the drive for innovation with crucial safeguards for fairness, transparency, and safety.

As we enter the next phase of the mind-machine relationship, characterised by increasing human-AI collaboration, the goal must be synergy, not replacement. AI should augment human intelligence, creativity, and judgement rather than diminishing them. Striking the right balance requires ongoing dialogue and collaboration among developers, users, policymakers, and the public to establish ethical guidelines and oversight mechanisms. Ensuring that AI complements human achievement, respects human values, and operates under meaningful human oversight is paramount for fostering a responsible and enriching future within the evolving world of mind sports and across the broader spectrum of human endeavour.

Geoffrey is CEO of IMSA and has a First-Class Honours Degree in Business Management and a Master’s Degree in Business Administration from Warwick Business School. He has been a Director of several companies and has worked as a consultant for listed companies.

Dmitry Fedotov is a Technology Expert and Innovator with extensive experience in blockchain, artificial intelligence, and emerging technologies. Involved in AI since 2008, Dmitry has consistently focused on the responsible integration of advanced technologies into strategic and regulatory frameworks. He currently leads regulatory blockchain and technology initiatives at Abu Dhabi Global Market (ADGM), though this article reflects his personal insights into AI governance and strategy in mind sports.