It has been over a year since Steven Forth’s blog The Ethics of Pricing AI, was published. In that post, Steven outlined key ethical challenges surrounding AI pricing, including transparency, fairness, and bias. Since then, the rapid adoption of AI technologies—particularly generative AI—has only intensified these concerns. As companies race to monetize their AI offerings, the need to balance profitability with accessibility has become a defining challenge for the industry.

In this blog, I aim to revisit and expand on the ideas Steven introduced, exploring how ethical considerations in AI pricing have evolved and what companies can do to ensure their pricing strategies remain fair, transparent, and inclusive.

The Evolving Landscape of AI Pricing Ethics

AI pricing has grown increasingly complex as generative AI applications become more diverse and impactful. From dynamic pricing models that adjust prices in real time to value-based approaches that tie price to outcomes, the tools available to pricing professionals are more powerful than ever. With great power comes great responsibility.

As Steven highlighted in his original blog, ethical concerns like transparency and fairness are central to pricing AI. But over the past year, new dimensions have emerged:

• Accessibility: How can companies ensure that their pricing models don’t exclude smaller businesses or underserved markets?

• Sustainability: How do we account for the environmental impact of AI infrastructure (e.g., energy-intensive GPUs) in pricing?

• Trust: How can companies design pricing models that build trust with customers while leveraging advanced algorithms?

These questions are no longer theoretical—they are pressing challenges that every company deploying AI must address.

Key Ethical Challenges in AI Pricing

1. Transparency: Building Trust Through Explainable Pricing

Transparency remains one of the most critical ethical considerations in AI pricing. As Steven noted last year, the rise of black-box algorithms has made it harder for both customers and sales teams to understand how prices are set. This lack of clarity erodes trust.

What’s Changed?

Over the past year, explainable AI (xAI) principles have gained traction as a way to address transparency concerns. The NIST framework for xAI outlines four key principles—Explanation, Meaningfulness, Explanation Accuracy, and Knowledge Limits—that are directly applicable to pricing algorithms.

Actionable Insight:

Companies should ensure their pricing algorithms meet xAI standards by:

• Providing clear explanations for how prices are calculated.

• Ensuring that explanations are meaningful and tailored to the intended audience (e.g., customers or internal sales teams).

• Regularly auditing algorithms to confirm their accuracy and adherence to ethical guidelines.

2. Fairness: Avoiding Bias in Pricing Models

As Steven discussed in his blog, bias is an inherent risk in machine learning models trained on historical data. In pricing, this can manifest as discriminatory outcomes that disproportionately impact certain customer segments or demographics.

What’s Changed?

The conversation around fairness has expanded to include not just bias in training data but also how algorithms adapt over time. Dynamic pricing models, for instance, may unintentionally favor high-margin customers while penalizing smaller businesses or nonprofits.

Actionable Insight:

To ensure fairness:

• Regularly audit pricing models for bias using diverse datasets.

• Implement guardrails that prevent discriminatory outcomes (e.g., setting minimum thresholds for underserved markets).

• Use value-based pricing approaches that align price with outcomes rather than customer characteristics.

3. Accessibility: Bridging the Gap Between Profitability and Inclusion

One area where ethical considerations have evolved significantly is accessibility. While high costs may be justified for enterprise clients using advanced features, they can create barriers for smaller businesses or organizations in emerging markets. This can shut out businesses and individuals who stand to benefit the most from these new solutions.

What’s Changed?

New approaches to AI like reasoning models come with real costs. See Pricing thought: OpenAI will price reasoning tokens in o1.

The rise of tiered and hybrid pricing models has provided companies with more flexibility to address accessibility concerns. For example, offering entry-level options at lower price points allows smaller customers to benefit from AI technologies without overcommitting financially.

Actionable Insight:

To promote accessibility:

• Develop tiered pricing structures that cater to a range of customer segments.

• Offer discounts or grants for nonprofits or educational institutions.

• Explore usage-based models that allow customers to pay only for what they use.

4. Sustainability: Accounting for Environmental Impact

While not addressed in Steven’s original blog, sustainability has become an increasingly important consideration in AI pricing. The energy demands of training large language models (LLMs) like GPT or Bard have raised questions about how environmental costs should be factored into pricing strategies.

See Is energy consumption and GHG emissions a concern in the development and operation of Large Language Models?

Actionable Insight:

Companies can incorporate sustainability into their pricing by:

• Highlighting energy-efficient features as part of their value proposition.

• Offering discounts for customers who adopt eco-friendly practices (e.g., cloud-based solutions with lower carbon footprints).

• Investing in renewable energy sources to offset the environmental impact of their operations.

Strategies for Ethical AI Pricing

Building on Steven’s original principles of transparency and fairness, here are additional strategies companies can adopt to balance profit with accessibility:

1. Adopt Outcome-Based Pricing:
   Tie prices directly to measurable outcomes achieved by customers (e.g., cost savings or productivity gains). This approach ensures customers only pay for the value they receive while promoting fairness across different segments

2. Leverage Generative Pricing Models:
   As discussed in Ibbaka’s work on generative pricing , this emergent approach blends dynamic and value-based pricing to create flexible models tailored to real-time customer needs. Generative pricing allows companies to adapt prices dynamically while maintaining alignment with customer val

3. Engage Stakeholders Early:
   Include customers, regulators, and internal teams in discussions about pricing ethics. This collaborative approach helps identify potential risks and ensures alignment with broader societal value

4. Invest in Ethical Audits:
   Regularly audit your pricing models for compliance with ethical guidelines around transparency, fairness, and sustainability. Use third-party experts when necessary to ensure objectivity

5. Treat people and companies of all sizes fairly and find ways to provide access

Case Studies: Companies Leading the Way

Case Study 1: OpenAI

OpenAI’s token-based model charges users based on consumption while offering free tiers for smaller users. This hybrid approach balances accessibility with profitability by allowing entry-level users to experiment without incurring significant costs.

Case Study 2: Microsoft Azure

Microsoft offers discounted Azure services for nonprofits as part of its commitment to accessibility. By tailoring its pricing strategy to underserved markets, Microsoft ensures its solutions remain inclusive while maintaining profitability.

Case Study 3: AWS

AWS uses tiered plans for its machine learning services, enabling startups and small businesses to access basic features at lower costs while providing advanced capabilities at premium price points. This approach promotes broad adoption without compromising revenue potential.

Looking Ahead

As AI technologies continue to evolve, so too must our approach to ethical considerations in pricing. Companies must go beyond maximizing revenue and focus on building trust through transparency, ensuring fairness through unbiased algorithms, and promoting accessibility through inclusive pricing strategies.