The theme of the 2025 Human Development Report from the United Nations Development Programme (UNDP) is artificial intelligence. The title: A matter of choice: People and possibilities in the age of AI.

Here are my thoughts (yes, I read the whole report. I’m exhausted.).

I would have liked more examples of things it says are going to work, things that are going to be good for people, especially in poor countries, or things that already have had problems (like when it says “Technological change can reinforce, amplify and reconfigure inequalities, potentially exacerbating discrimination or generating new forms of it” but then doesn’t offer examples – and the examples, which I have been tracking, are horrific).

It cheerily says things like

AI presents multiple opportunities for augmenting what people are already doing at work. It can help workers complete tasks faster and at higher quality, boost their creativity and speed up learning processes… on page 167. But then doesn’t provide examples of this. It should be PACKED with examples of what it says works oh-so-well.

And this should have opened the report – but it’s buried on pages 139 and 140:

We live in a novel social reality where algorithms (many of them AI-based) mediate many of our social relations and shape much of our engagement with the world. Whether through social media, search engines, online shopping or digital communication tools, algorithmic intermediaries are reshaping the landscape of human-to-human interactions, defining the context and boundaries within which people engage.

They could have thrown in what we watch: I would say 70% of the people in my life make the choices on what to watch based on what an algorithm tells them to on a streaming service.

Lots more of these observations, way too buried in the report:

As the amount of information available in our increasingly digital world continues to expand, recommender algorithms channel our attention, seeking what is relevant to each person. A core challenge of leveraging the internet for human development is that the information people use to promote their own agency and improve their capabilities far exceeds what anyone can reasonably consume. To overcome this limitation, algorithmic tools to search and filter information have come to define the modern internet. From early web searches and later social media feeds to modern chatbots, our experience of the internet is filtered through some form of algorithm, often AI-based recommender systems. From page 141.

By shaping power relations between the people they mediate, algorithmic intermediaries enable some users to exert influence over others, affecting their prospects and choices. Moreover, as a result of numerous, repetitive social interactions, recommender systems are reconfiguring societal structures, including social norms, institutions and culture—reshaping political discourse and deliberation. From page 143.

I didn’t like how buried these observations are, coming after about 100 pages of AI IS AMAZING!!! narrative.

But overall, the report is a worthwhile read and I do like it.

My favorite part is Part 4: Framing narratives to reimagine AI to advance human development. It’s focused people with disabilities and elderly people with regard to AI and tech innovations. It’s realistic and it busts a LOT of hype. It calls out tech bros for telling people with disabilities what they need in AI and other tech innovations without asking first, and for thinking all elderly people are old, frail and about to fall at any given moment.

As usual, it has to have reminders that should be obvious, like:

gender inequalities in the design and use of AI result not from women’s lower technological aptitude, interest or skills. Rather, they arise from discriminatory social norms that construct technology as masculine and devalue women’s expertise, knowledge and contributions. Therefore, closing gender gaps, perhaps by increasing access to technology and digital skills training—crucial as they are—may not be enough. The focus needs to be on expanding women’s agency to not just benefit equally from technological change but to shape technological developments that reflect and actively promote equity and social change. (page 117)

and

Transformative social change can take place when innovations in AI are designed by a diverse group of developers, including women and people from other marginalized and intersecting identities; when those innovations recognize and address social norms and imbalances; and when they are backed by changes in policies and institutions. (pages 118 – 119)

and

AI reflects the biases and stereotypes in the data on which it is trained.

And the data is sexist and racist -let’s be clear, that IS the reality. An article from UN Women, How AI reinforces gender bias—and what we can do about it, says more.

I liked this caution, and wish it had come much earlier:

When human involvement in work is diminished, it can lead to moral disengagement, where individuals become detached from the ethical and behavioural norms that usually guide their actions. When people feel disconnected, their sense of accountability may diminish, increasing the risk of errors and safety issues—especially in highly automated settings. Algorithmic management systems, designed to improve efficiency through monitoring and automation of work allocation, may instead increase errors and disrupt entire workflows if they push workers to engage in multitasking and to oversee simultaneous workflows at ever higher speed. Similarly, digital surveillance in the workplace— including email monitoring, keystroke tracking and social media scrutiny—can create considerable psychological stress for employees. While these practices aim to enhance productivity and data security, they also contribute to workplace anxiety. Employees can feel a loss of freedom and trust when subjected to excessive surveillance, reducing their motivation and job satisfaction. From pages 171 and 172

the allure of AI has created an image of almost completely autonomous systems, nearly free from human intervention beyond the brilliant programmers who developed them.89 In reality, AI depends heavily on human workers in every step of the supply chain. Lower-value-added activities, such as data labelling and annotation, are often concentrated in low- and middle-income countries, requiring intensive human labour but offering limited rewards. In contrast, higher-value-added tasks, such as AI model design and deployment, are confined largely to high-income countries, demanding specialized knowledge and infrastructure.90 The reliance on human labour across the AI supply chain highlights the need to examine who contributes to AI systems, under what conditions and how the value they create is distributed… A complementarity economy recognizes and values workers at every stage of the supply chain, towards ensuring meaningful opportunities, fair compensation and decent working conditions. The future of work in the age of AI should be one of genuine collaboration between humans and machines—not one built on a hidden global workforce facing decent work deficits. From page 172.

Pretty clear that NO ONE from DOGE has read any of the extensive research material cited in this report – and won’t read this report either. Nothing being done at the federal level by them follows any recommended or human-focused practice whatsoever.

Note: The United Nations Educational, Scientific and Cultural Organization’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, provides a global policy framework for guiding AI use to uphold human rights and dignity and ensuring that AI benefits societies at large. Updated in 2024, the OECD AI Principles are another set of intergovernmental standards on AI, with 47 adherent countries, providing a basis for developing AI that respects human rights and democratic values.

All that said: please don’t comment unless you have actually read the report.

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