Using AI to increase effectiveness of human-wildlife conflict mitigations for WAW (Rethink Priorities)

1. Overview

This report explores using artificial intelligence (AI) to increase the effectiveness of human-wildlife conflict (HWC) mitigations in order to benefit wild animal welfare (WAW). Two concrete examples are providing more funding, research and direct work into reducing fatalities due to 1) collisions between bats and wind turbines, and 2) culling crop-raiding starlings. The report aims merely to raise awareness of this topic and introduce the idea for discussion, but not yet strongly suggest it is a cost-effective intervention on par with other interventions-see uncertainties, limitations, and potential for harm.

What’s the problem profile?

• HWC is increasing due to human expansions and climate change, (Gross et al., 2021) and is starting to be considered in government strategies and policy. The expected future impact of innovative and effective solutions to HWC could be even larger than currently appreciated.
• Lethal control or other methods which significantly impact animal welfare are still widely used (such as culling), despite preventative non-lethal strategies growing in more recent wildlife management approaches.
• Currently deployed AI systems directed towards HWC could be expanded further within the next 10-20 years as they become more reliable, more effective, and cheaper. We should not assume they will prioritize WAW concerns, or be widely used for animals of WAW concern, so this should be embedded before they are potentially rolled out at scale.
• There are already companies working on AI solutions for specific problems involving endangered species, such as protected areas using AI assisted technology for poacher detection. There is already proof-of-concept of an NGO-backed early warning AI system, ‘WildEyes’, with this type of solution being invested in by a local governmental department in Tamil Nadu, India. Buy-in from a range of stakeholders (especially when it benefits humans and profits too) offers a way in with conservationists and researchers who may not otherwise consider WAW. Research and development (R&D) on AI-assisted HWC mitigations would likely attract researchers who would not otherwise consider or be motivated by WAW concerns.

What should we be doing differently?

A very tentative theory of change: if machine vision-based methods prevent HWC, they could be adopted, even on a small scale → helps drop prices → allowing for systems to be more widely adopted → leads to more support and R&D → continued price drops and adoption → could create space for legislation to ban harmful or lethal methods of animal control → preventing HWC could reduce apathy and antagonism towards “problem species” and make it easier for people to consider the welfare of animals, while also directly reducing negative WAW effects of HWC.

• This report highlights two examples of HWC where advocates could influence AI-assisted mitigation to directly affect substantial numbers of animals, and spread welfare considerations in software and norms:
  • Wind turbine collisions are a leading anthropogenic cause of bat deaths and cause a significant number of bird deaths (600,000 to 949,000 bats and 140,000 to 679,000 birds annually in North America). We should expect fatalities to increase due to expansions in wind power.
  • Culling of crop-raiding species. In one year, the USDA’s Wildlife Services culled 1,028,642 European starlings responsible for agricultural crop damage, because other mitigations are ineffective. Despite this, starlings still cause extensive damage each year. More effective mitigation measures would hold value and could prevent culls.
• There are a number of research and advocacy directions to address these:
  • Conduct cost-effectiveness estimates on different HWC mitigations; in some cases, Randomized controlled trials (RCT) and Cost-Effectiveness Analyses (CEAs) may find mitigations other than AI-assisted ones are more effective.
  • Improved research and data collection of how many animals HWC affects. For example, estimating bird and bat turbine fatalities is difficult, (hence wide ranging estimates); utilizing cameras and automated detection could help us collect more accurate data.
  • Research on improving accurate small object detection from a distance, so that automated detection and curtailment systems at wind farms can reliably detect smaller species.
  • Utilizing machine vision techniques in long-wave infrared cameras placed near wind turbines could allow them to detect bats and avoid fatalities.
  • Researchers could apply machine learning techniques, utilizing large amounts of existing data on well-studied animal populations, or use machine vision to gather this data to predict where mitigation will be most effective. E.g., identifying high-risk areas for wildlife where turbines should not be erected.
  • R&D into flock detection systems which could be integrated with automated deterrents to prevent wide-scale crop damage by flocks of starlings – if effective, this could be adopted in favor of wide-scale culling.
  • Extending protected status to a wider number of bats, and utilizing this to get leverage over wind energy companies.
  • Adding more bird species/a species with a large population to protected species acts and/or automated curtailment detection lists covered by already deployed AI mitigation technologies could reduce their fatalities by >60%.
  • Funders and researchers can also engage in institution-building to create a robust interdisciplinary field at the intersection of AI development and wildlife management:
    • Establish an interdisciplinary conference tying experts from engineering, AI, animal welfare, ecology, animal behavior, conservation, and perhaps even from the social sciences to discuss examples and opportunities for AI to increase the effectiveness of HWC mitigations, or even wildlife management methods more generally, that improve animal welfare.
    • Create an accessible applied methods journal for publication of this interdisciplinary work.
• See section 3 for the uncertainties and section 4 for the limitations of this report.