imperfect offerings

Imperfect offerings is a Substack that critically explores the intersections of technology, education, and social issues, with a focus on the implications of AI development. It covers the ethical, economic, and political dimensions of technological advancements, especially in academia, addressing concerns such as bias, privacy, and the future of work.

Artificial Intelligence Education Ethics in Technology Future of Work Digital Privacy Technology and Society Academic Practices Environmental Impact of Technology AI in Healthcare Critical Thinking

The hottest Substack posts of imperfect offerings

And their main takeaways

Looking for a better story

239 implied HN points • 18 Mar 24
🕹 Technology AI Ethics Environment Policy
  1. The future of AI may not necessarily be as promising as it has been hyped, with concerns about inflated expectations and potential limited use cases.
  2. The use of generative AI can have unintended negative consequences, such as detrimental effects on academia, exploitation of data workers, and potential harm to minority languages.
  3. AI's impact on the environment, from excessive water usage to electricity consumption, raises concerns about accelerating climate change and misinformation.

Things don't only get better

379 implied HN points • 26 Feb 24
🕹 Technology AI Climate Workforce Education Models
  1. Improvements in AI models are not always guaranteed, as evidenced by instances of models getting worse over time due to tweaks and updates.
  2. Investment in AI technology is booming, generating wealth for billionaires while possibly hindering investment in viable low-carbon tech solutions for climate change.
  3. The narrative surrounding AI portrays it as a powerful force for the future, but practical solutions for climate crisis require more than just technological advancements - they also need systemic changes and investments.

Deepfake pedagogy

319 implied HN points • 24 Feb 24
🕹 Technology AI Video Education Media Ethics
  1. Synthetic media like deepfake videos raise concerns about truth and authenticity, impacting education and public discourse.
  2. The development and use of AI-generated media like Sora in elections and public communication can distort reality and trust in information.
  3. Educators need to focus on critical thinking, authentic assessment, and personal engagement to navigate the challenges posed by synthetic media in learning environments.

Who pays for 'authenticity'?

199 implied HN points • 12 Mar 24
🕹 Technology Generative AI Expertise
  1. Universities are investing in AI literacy for their staff and students, covering various important topics like privacy, bias, and ethics.
  2. Peer-supported discovery and open education communities play a crucial role in empowering individuals to engage with new technologies.
  3. The development and use of generative AI models come with challenges related to bias, authenticity, and the trade-offs between safety and performance.

Never mind the quality, feel the speed

239 implied HN points • 02 Feb 24
🚌 Education Research AI Data Modeling Social Sciences
  1. The research economy is increasingly focused on speed over quality, especially with the rise of generative AI, which can have negative impacts on reproducibility and diverse fields of knowledge.
  2. Data models in research need to be carefully scrutinized for accuracy and not blindly relied upon, even in specialized areas like protein folding, climate science, or medical diagnostics.
  3. Speed and heuristics shouldn't overshadow the importance of deliberation, qualitative research, and embracing complexity in arriving at meaningful solutions to multidimensional problems.
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Gods, slaves and playmates

139 implied HN points • 26 Feb 24
🕹 Technology AI Big Data Automation Ethics
  1. The essay/post explores AI fantasies and their significance in education.
  2. People tend to relate to synthetic models as if they have agency, even though they don't.
  3. Big tech industry creates a narrative around AI as gods or monsters, while in reality, these AI systems are often designed to serve in subservient roles.

AI rights and human harms

219 implied HN points • 17 Jan 24
🕹 Technology AI Ethics Rights Law Human Rights
  1. AI industry co-opts the term 'learning' to justify its innovations and obscure responsibilities
  2. There is a call for an AI rights movement, drawing parallels with animal rights that may oversimplify complex ethical issues
  3. Human rights are at risk when powerful corporations prioritize their interests over accountability and regulation in the development and deployment of AI technology

Risks to knowledge economies

219 implied HN points • 10 Jan 24
🚌 Education Online Learning
  1. Risks to knowledge economies are being highlighted in relation to generative AI and its potential impact on universities and academic practices.
  2. The use of generative AI platforms can lead to inequalities in knowledge production and amplification of existing biases and disparities.
  3. Open knowledge projects like Wikipedia are facing challenges from generative AI, with potential impact on diversity and community-driven content creation.

'Human intelligence'

13 HN points • 10 Apr 24
🚌 Education AI Data Work Learning Assessment
  1. The concept of 'artificial intelligence' has historically been used to define and value 'intelligence', leading to discriminatory practices in education and beyond.
  2. The term 'human intelligence' has been co-opted by the AI industry to alleviate concerns about job displacement, but in reality, it devalues certain types of work and people, especially those involving care and emotional labor.
  3. The comparison between artificial and human intelligence creates a double bind for students and workers, expecting them to conform to data-driven systems while also being 'more human', which can lead to confusion and anxiety.

Whose ethics? Whose AI?

159 implied HN points • 03 Jan 24
🚌 Education Ethics AI Learning Teaching Research
  1. Building an ethical ecosystem for AI in academia requires collaboration and coordination within the sector to meet regulatory requirements and promote openness.
  2. Designing assignments that make the use of generative AI tools less compelling can enhance learning outcomes and reduce the need for detection methods that undermine trust.
  3. Individual educators should challenge the idea that students can act ethically in a context lacking supportive infrastructure for informed ethical decision-making, and focus on conversations about writing practice to foster understanding and development.

AI and the privatisation of everything

259 implied HN points • 04 Nov 23
🕹 Technology AI Data Privacy Healthcare Education Government Relations
  1. Generative AI can reshape relationships at personal and societal levels through its integration into everyday life and work.
  2. The use of AI in privatising public goods like healthcare and education raises concerns about data control, accountability, and the concentration of knowledge and power in the hands of few corporations.
  3. AI facilitates the privatisation of public services through the capture of expertise, turning professionals into consumers of recycled expertise and potentially diminishing the role of teachers and healthcare providers in favor of automated systems.

Imperfect, but sometimes on the money

179 implied HN points • 24 Nov 23
🕹 Technology Data Privacy AI EdTech Recruitment Media Literacy
  1. Peter Thiel's Palantir has taken over the federated data service for the NHS, impacting data sharing opt-outs for patients and raising concerns about private interests in public health data.
  2. In the education sector, AI's influence, particularly in EdTech, raises issues around data governance, privacy regulations, and the challenge of regulating online platforms.
  3. AI's expansion into various sectors, including recruitment, poses challenges such as potential bias, pricing out of students, and the use of AI for assessments, leading to a possible 'AI-driven race to the middle' in hiring practices.

Techbros party on

119 implied HN points • 01 Nov 23
🕹 Technology AI Regulation Tech Giants Ethics Innovation
  1. The 'Safer AI Summit' had predictable guest choices, with figures like Elon Musk and senior representatives of tech giants invited, focusing more on future AI developments than present issues.
  2. The summit had strict restrictions on discussion topics, limiting conversations solely to the risks and opportunities of frontier AI, ignoring broader societal impacts.
  3. Criticism was raised against the summit for being exclusive, favoring big tech corporations, and shutting out voices from trade unions, civil society groups, and organizations concerned about AI ethics.

AI literacy and Schrödinger's ethics

179 implied HN points • 14 Jul 23
🚌 Education Ethics Regulation
  1. Universities are emphasizing AI literacy and ethical use of AI tools for students and staff in education.
  2. There is a call for the development of independent codes of ethics and practices in universities to address the unique risks and challenges posed by AI in education.
  3. The responsibility falls on teaching staff to navigate the complex decisions around AI use, considering ethical implications and potential harms.

'Luckily, we love tedious work'

119 implied HN points • 24 Aug 23
🚌 Education Workplace dynamics Artificial Intelligence Humanities Critical Thinking
  1. Generative AI may impact the job market, emphasizing marketization over addressing economic and social challenges.
  2. Artificial intelligences may free humans from tedious tasks, but can also lead to uncreative and repetitive work.
  3. AI technologies are evolving, but their impact on graduate job market transformation may not align with initial expectations.

Labour in the middle layer

139 implied HN points • 20 Jul 23
🕹 Technology AI Labor Automation Tech industry
  1. Human work plays a crucial role in maintaining the illusion of intelligence in AI models by performing tasks like reviewing outputs and assigning ratings.
  2. The human labor in the middle layer of AI development is extensive, complex, and ongoing, despite being often overlooked by the industry.
  3. Students and graduates are increasingly becoming involved in platform data work, which can impact their job satisfaction and well-being, raising questions about the future of labor in the AI industry.

"0% detection has never felt better"

119 implied HN points • 07 Aug 23
🚌 Education Risk Analysis Academic Integrity Ethical Concerns
  1. Generative AI tools may fail to expose users to diverse ideas and perspectives, reinforcing existing biases.
  2. There is a risk that the use of generative AI may not respect human rights and safeguard individual autonomy, especially for children.
  3. It is important for educators to carefully consider the consequences of incorporating generative AI tools in teaching, ensuring fairness, transparency, and accountability.

Life is imperfect

79 implied HN points • 31 Aug 23
🕹 Technology EdTech AI Surveillance Content creation
  1. Life is imperfect - The message shared is about life's imperfections and how it plays out in different settings, emphasizing the need to navigate through challenges.
  2. Criticism in edtech - Discussion on the critique of ed tech companies' practices, highlighting the need for addressing power imbalances and engaging with critical voices.
  3. Generative AI impact - Insights into how generative AI is affecting graduate employment, the restructuring of labor, and the broader impact on work routines and value.

Writing as synthesis

59 implied HN points • 01 Oct 23
🕹 Technology AI Education Writing Generative AI
  1. Generative AI is being regulated in industries like Hollywood to ensure human writers receive proper credit and compensation even when AI-generated content is used in the development process.
  2. The future of AI in education presents opportunities for collaborative efforts to create public sector language models, potentially shifting costs to governments for developing foundational models for various languages.
  3. Vygotsky's perspective emphasizes how generative AI tools should engage humans in advanced thought processes and interpersonal activities, rather than just producing text, sparking questions about learners' interactions and collective knowledge production.

Writing as academic practice in an time of Generative AI

99 implied HN points • 28 Jun 23
🚌 Education Technology Accountability AI writing
  1. Educators question the role of generative AI in student writing assignments, suggesting alternative tasks like critical evaluation of AI-generated text.
  2. Generative AI tools like ChatGPT are a part of a timeline that includes various writing tools like spell checkers and translation engines, impacting writing practices of students and academics.
  3. Students and educators should focus on accountable writing tasks that center aspects that AI technology may struggle with, such as developing original ideas, understanding audiences, and negotiating perspectives.

On language, language models and writing

119 implied HN points • 21 Apr 23
🚌 Education Language AI Academic Integrity Future of work
  1. AI tools like language models cannot be credited with authorship in academic publications due to lack of accountability and responsibility for the work.
  2. Universities need to consider the implications of students using AI writing tools and ensure they are transparent, accountable, and responsible for their own use of these systems.
  3. Writing is a social technology that shapes new selves and identities, and universities play a crucial role in shaping what writing is, what it does for individuals, and why it matters.

Human work, GenAI and Mechanical Turks

79 implied HN points • 11 Jul 23
🕹 Technology AI Labor relations Machine Learning
  1. Technology like GenAI can be viewed as a platform for coordinating labor, shaping relationships between users, owners, and revenue sources.
  2. The development of GenAI involves complex layers of human labor, from providing training data to post-training alignment through human feedback.
  3. The economic structure surrounding GenAI results in the extraction of value for platform corporations, while the vast majority of human labor involved in its development remains unpaid or underpaid.

GenAI opportunity and risk

79 implied HN points • 26 Jun 23
🚌 Education Research Technology Learning Assessment AI
  1. Researchers, policy-makers, educators, and edtech activists are raising concerns about the use of GenAI in teaching and learning, highlighting issues such as inaccuracy, bias, and ethics.
  2. Balancing the opportunities and risks of GenAI is crucial, as technologies are designed for use and may present actual harms over time that are harder to research.
  3. Cutting through the hype surrounding GenAI, the real opportunities involve improving efficiency in textual production and providing natural language interfaces for accessing information, but careful consideration is needed to ensure true educational value.

Back to college with generative AI

39 implied HN points • 18 Sep 23
🕹 Technology AI Education Tech Integration Ethics Policy
  1. Generative AI is being integrated into various platforms and tools, such as Microsoft Office, making it more accessible to business users.
  2. Educators are facing new challenges in incorporating generative AI into teaching practices while maintaining responsible and ethical use.
  3. Resources and guidelines are available to help teachers and students navigate the use of generative AI in education, emphasizing ethical and critical considerations.

Risks to teaching as work and teachers as workers

39 implied HN points • 17 Jul 23
🚌 Education Teaching AI EdTech Workforce
  1. Teachers are vulnerable to automation and AI tools that could change the nature of their work and how it's valued.
  2. AI has the potential to impact various professions beyond teaching, such as journalism, acting, music, and art, through automation of tasks and production.
  3. The use of AI in different sectors, driven by profit motives, can lead to job insecurity and challenges to worker's rights across industries.

Skynet scenarios and real world risks

39 implied HN points • 16 Jul 23
🕹 Technology Artificial Intelligence Ethics Education Security
  1. Opportunities and risks should be treated differently; risks are harder to see and require collective action to address
  2. Education has a responsibility to develop critical users of technology to navigate the risks associated with GenAI
  3. Higher education should identify and speak up about risks specific to teaching jobs, student development, and knowledge values in relation to GenAI

Student assignments in a time of language modelling

19 implied HN points • 21 Apr 23
🚌 Education Teaching Curriculum Writing Technology Critical Thinking
  1. Educators can design accountable writing assignments to help students develop critical thinking skills and focus on aspects of human writing that large language models struggle with.
  2. Encouraging students to write from different positions or points of view, reflecting on personal experiences, and engaging in writing as part of a community can enhance accountability and support the development of writing skills.
  3. Using language model tools critically involves questioning their accuracy, biases, and potential impacts, while utilizing them creatively should be balanced with considering the limitations and risks associated with these tools.

New offerings

0 implied HN points • 22 Apr 23
🚌 Education AI Surveillance Critical Thinking
  1. Helen Beetham is developing various pieces like 'The platform university', AI illusions, and pedagogies of anti-surveillance.
  2. The content in development includes chapters on critical thinking in the digital university, post-digitality, and more.
  3. Helen Beetham's future work will cover topics such as learning design, learning spaces, and other relevant mainstream pieces.