Cost Sensitive Learning – A Hitchhikers Guide

Typically prediction is about getting the right answer. But many prediction problems have large and asymmetric costs for different types of mistakes. And often, the chance of making mistakes is exacerbated by training data imbalances. Cost-Sensitive Learning is the range of techniques for extending standard ML approaches to deal with imbalanced data and outcomes. Cost-sensitive predictions will instead favour the most valuable or lowest risk answers.

I presented Cost Sensitive Learning – A Hitchhikers Guide at the Melbourne ML/AI Meetup.

Step Up on AI

I provided commentary on our need to step on AI capability and governance in Australia on this story in The Australian newspaper.

I was quoted extensively in the article but I wrote a bunch more notes which might be of interest.

Further Commentary

  • In the regrettable case of the Knightscope security robot and the curious case of Facebook bots, we should consider governance of product development as well as unexpected behaviours of AI systems. Governance in product development means introducing beneficial innovations in a safe manner. In self-driving vehicles, we could contrast the measured approach of Waymo (Google) – incorporating test tracks, an extensive simulation program and human supervision – to the cavalier approach of Uber – flouting state regulations to rush vehicles onto public streets. The Knightscope case may be a failure to discover, design against, and test the product’s potential failure modes in a safe environment, rather than an inherent failing of AI. The Facebook case demonstrates the value of being able to actively discontinue product research without any wider adverse effects. This is not to deny sometimes unexpected behaviours of AI systems, or deny the risk posed by poorly governed product development, but rather to focus the conversation on how to safely harness the benefits of AI products.
  • Ethical and regulatory frameworks are valuable and necessary, as AI is among our most powerful technologies. There are a number of valid concerns based on bad or worst case scenarios for weaponisation, mass displacement of workers, systemic data-driven discrimination, the erosion of democratic society, hostile self-improving systems, etc. With agreed ethics and global frameworks for AI research and development, customer and citizen data regulations, and active governance of wider societal change, we can benefit from AI without exposing ourselves to worst case scenarios. Given that ethical and regulatory frameworks and broader policy changes will take some effort to establish and incentives will remain for actors to circumvent these frameworks, I also think that education and the private sector have major roles to play to improve understanding and bridge gaps short term.
  • Education is key to an informed discussion about the benefits and risks of AI. The challenge is for institutions is to keep up with the state of the art. We need to create forums for public and private researches and product developers to engage with policy makers and other public institutions. And then we need evidence-based policy formulation. Australian primary schools already teach cybersecurity in early years; let’s bring AI into the curriculum too. Education at all levels should go beyond a fundamental understanding of AI to developing the skills needed to contribute to, thrive in, and continue to shape a workplace and society where many routine cognitive tasks are automated.
  • The private sector is already providing some responses to ethical challenges in the absence of regulation. For instance, Volvo has stated it will “accept full liability whenever one if its cars is in autonomous mode”. Technology companies – aware their social license is being eroded by issues central to AI such as “filter bubbles”, programmatic advertising alongside objectionable content, and mass data collection – are introducing features designed to benefit users and citizens. Examples include BuzzFeed’s Outside Your Bubble, Google’s recent YouTube ad restrictions, and Apple’s CoreML support for AI on-device to maintain data privacy. We should also be encouraging the Australian private sector to take a leadership role and developing the technology and governance expertise to enable this.w
  • Australia could be a leader in this field. We do have many of the right ingredients. However, the current reality is that the EU is leading regulatory change, with 2018’s General Data Protection Regulation set to extend existing data provisions to effectively create a “right to explanation” for users about whom algorithmic decisions are made. GDPR is already affecting Australian organisations with European operations. GDPR could require a complete overhaul of common AI approaches and is driving research into making AI systems more understandable. For Australia to take the lead in setting the global AI agenda would further require a different type of domestic politics to what we have seen over the last decade in respect of pressing global and technology issues, such as climate change and energy.

General Background

  • Machines (AI) are outperforming humans in a range of narrow, intuitive cognitive tasks. Some major themes have emerged: supporting human interactions, and playing games. For instance, one study showed machines are better lip-reading than humans. They are also better players of the complex strategy game of Go, better at bluffing in Poker, and better at the most advanced computer games. However, with ongoing digitisation of the physical world, business and society, we will see more and more examples of narrow human intuition, or “know-how”, bettered by machines. 
  • The primary technology driving the leap is Deep Learning, also known as Artificial Neural Networks. Deep Learning is delivering substantial performance improvements in fields such as language processing and translation, medical diagnosis, and self-driving vehicles (in addition to human interactions and games above). Rather than attempting to explicitly define and program algorithms and statistical matching, Deep Learning leverages large data sets to train Neural Networks to recognise patterns and learn their own internal representation of key concepts.
  • While the core ideas behind Deep Learning are not new, three major technology developments have boosted the capabilities of networks in the order of 1000 times in the last five years: massive web-scale data sets, development of dedicated parallel hardware, and innovation in network designs. These technology advancements have created a positive feedback loop of research and development funding, so innovation continues at a rapid pace.
  • This new technology capability creates enormous opportunity, but also disruption and risk. Inhuman feats of decision-making are possible, and might help solve pressing global problems in the environment, health and society. Deep Learning systems can be wider thinkers (across far bigger data sets) and more scalable thinkers (in speed or other dimensions) than humans, and they also notice patterns that humans miss or cannot perceive. However, humans remain faster learners and more flexible thinkers. And as machines best humans they also better humans – the machine Go champion AlphaGo is now training a new generation of human Go champions. The key to opportunity is not to simply outsource human “know-how” to machines, but rather to enhance human know-how with machine intuition in order to drive new breakthroughs in understanding cause-and-effect in complex systems. In this respect, Human Centred Design is a key complement to machine intuition.
  • The automation of a wide range of narrow cognitive tasks will undoubtedly be disruptive, but through education and pro-active governance, we can make this a positive disruption. As people are displaced from jobs or their jobs change substantially, we must create new societal and commercial constructs that confer purpose, enable people to contribute to, thrive in, and shape a world enabled by AI, and provide them resources for participation in an equitable society. The true nature and extent of the disruption remains to be seen, as prior automation of routine manual tasks has also enabled growth in non-routine, creative and community-oriented manual tasks. This disruption is a huge opportunity to reimagine work and society for the better. 
  • Risk exists in the fact that the operation of AI products may not be adequately explainable, exacerbating the reality that they have new intrinsic failure modes, can be attacked and exploited externally, and can ultimately be developed with malicious intent. Key failure modes include biased decision making-based on biased training sets, and the creation of self-fulfilling prediction and action feedback loops. These failure modes further often disproportionately affect the most vulnerable members of society, as they are not adequately captured in training data or noticed in feedback loops, and may in the worst case curtail basic democratic freedoms. However, systems based on human decision making are similarly fallible, and further suffer from execution variability, such as the estimated 90% of road incidents caused by human error, so we need to take a holistic approach to societal benefit and risk in these circumstances. These issues also intersect with broader concerns about data collection and privacy. Given that Deep Learning systems match patterns, it is possible to engineer false positives through study of the system, and in fact secondary Deep Learning systems may be applied to this task! We may well see a new cyber AI arms race to protect and exploit AI systems. However, this may also spawn much needed explanations of how these systems work. Tackling development with malicious intent requires global coordination on ethical and regulatory frameworks, and also addressing the root causes of intent. Ethical and regulatory frameworks should as a precautionary measure consider the broader existential risk posed by the possibility of a hostile self-improving artificial general or super intelligence. However, the eventual development of such an intelligence is not a foregone conclusion.
  • Ultimately, understanding the current wave of AI development better will enable individuals and private and public organisations to better capture opportunity while governing risk. We should invest in research and education and develop new mechanisms and institutions to shape the development of this technology for the best possible ends. The payoff will be beyond functional, as understanding these new capabilities of machines will lead to a deeper understanding of what it means to be human.

Reasoning About Machine Intuition

This talk discusses the resurgence of Machine Learning and neural networks from multiple perspectives of digital delivery, including: product & design, iterative implementation, organisational design, governance and risk. I chose to use “Intuition” to distinguish ML’s capability for pattern recognition from other descriptions of intelligence. Slides here.