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UN and Red Cross call for urgent rules on autonomous weapons
The appeal says a legally binding instrument is needed to keep human control over lethal force as AI weapons advance at record speed.
- On Tuesday, the United Nations and the International Committee of the Red Cross warned the world is "dangerously close" to autonomous weapons targeting humans, urging nations to begin negotiations on a legally binding treaty.
- Discussions regarding rules for autonomous weapons have been ongoing since 2014, yet conflicts in Ukraine, Gaza, and Sudan have intensified fears that AI-assisted systems could soon cross into lethal autonomous targeting.
- Autonomous systems struggle to distinguish between combatants and civilians in volatile environments, creating significant obstacles to establishing legal accountability for potential war crimes and escalating lethal error risks.
- Ahead of the CCW Review Conference in November in Geneva, the UN and Red Cross called for "political courage," urging countries to decide whether to launch negotiations after three years of discussions.
- "Innovation must serve humanity, not endanger it," said United Nations Secretary-General Antonio Guterres and Red Cross President Mirjana Spoljaric Egger, noting the race toward greater autonomy in weapon systems is already underway.
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"Do not act will cost lives."They renew their call to regulate these machines, which are already in use.
·Montreal, Canada
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The heads of the UN and the ICRC warned on Tuesday of the risk posed by the development of autonomous weapons in the world, particularly by leaving the decision to kill human beings to machines.
·Issy-les-Moulineaux, France
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World urged to rein in ‘killer robots’
GENEVA - The United Nations and the Red Cross warned Tuesday that the world is “dangerously close” to a future where so-called “killer robots” could target humans, and issued an urgent appeal for international regulation of autonomous weapons.
·Bangkok, Thailand
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Total News Sources58
Leaning Left17Leaning Right12Center21Last UpdatedBias Distribution42% Center
Bias Distribution
- 42% of the sources are Center
42% Center
L 34%
C 42%
R 24%
Factuality
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