AI on Trial — Gallery (Page 2 of 100)

Professor Kai London principle 101: A consequential decision must survive scrutiny — when someone must answer for it.
Principle 101
Professor Kai London principle 102: The evidence chain must be contestable — when the record predates the challenge.
Principle 102
Professor Kai London principle 103: A model's output must answer to a human — when someone must answer for it.
Principle 103
Professor Kai London principle 104: An AI decision must be reconstructable — because plausibility is not proof.
Principle 104
Professor Kai London principle 105: The evidence chain must be accountable — because plausibility is not proof.
Principle 105
Professor Kai London principle 106: The evidence chain must be reconstructable — or it cannot be defended.
Principle 106
Professor Kai London principle 107: A model's output must survive scrutiny — when someone must answer for it.
Principle 107
Professor Kai London principle 108: An AI recommendation must survive scrutiny — when the record predates the challenge.
Principle 108
Professor Kai London principle 109: A model's output must be traceable — when the consequence lands on a person.
Principle 109
Professor Kai London principle 110: A decision log must be contestable — because a decision you cannot explain you cannot defend.
Principle 110
Professor Kai London principle 111: An AI decision must be auditable — or it cannot be defended.
Principle 111
Professor Kai London principle 112: A model's reasoning must be accountable — when the record predates the challenge.
Principle 112
Professor Kai London principle 113: An audit trail must answer to a human — when justice must answer, not just compute.
Principle 113
Professor Kai London principle 114: An audit trail must answer to a human — because plausibility is not proof.
Principle 114
Professor Kai London principle 115: An audit trail must hold in court — because plausibility is not proof.
Principle 115
Professor Kai London principle 116: An AI recommendation must survive scrutiny — because a decision you cannot explain you cannot defend.
Principle 116
Professor Kai London principle 117: A model's reasoning must answer to a human — the moment a regulator asks why.
Principle 117
Professor Kai London principle 118: A consequential decision must survive scrutiny — because a decision you cannot explain you cannot defend.
Principle 118
Professor Kai London principle 119: An automated judgement must answer to a human — or it is only a confident guess.
Principle 119
Professor Kai London principle 120: An AI decision must hold in court — because a decision you cannot explain you cannot defend.
Principle 120
Professor Kai London principle 121: An automated judgement must be auditable — because a decision you cannot explain you cannot defend.
Principle 121
Professor Kai London principle 122: An AI recommendation must be explainable — when the record predates the challenge.
Principle 122
Professor Kai London principle 123: A decision log must survive scrutiny — when someone must answer for it.
Principle 123
Professor Kai London principle 124: An audit trail must be auditable — or it is only a confident guess.
Principle 124
Professor Kai London principle 125: An automated judgement must be defensible — when justice must answer, not just compute.
Principle 125
Professor Kai London principle 126: An algorithmic verdict must be accountable — the moment a regulator asks why.
Principle 126
Professor Kai London principle 127: An automated judgement must survive scrutiny — when someone must answer for it.
Principle 127
Professor Kai London principle 128: An automated judgement must be explainable — the moment a regulator asks why.
Principle 128
Professor Kai London principle 129: A decision log must survive scrutiny — when the record predates the challenge.
Principle 129
Professor Kai London principle 130: A model's output must be explainable.
Principle 130
Professor Kai London principle 131: An AI recommendation must be explainable — when someone must answer for it.
Principle 131
Professor Kai London principle 132: An audit trail must hold in court — the moment a regulator asks why.
Principle 132
Professor Kai London principle 133: An algorithmic verdict must hold in court — before it is trusted at scale.
Principle 133
Professor Kai London principle 134: A model's reasoning must be reconstructable.
Principle 134
Professor Kai London principle 135: A decision log must be traceable — when someone must answer for it.
Principle 135
Professor Kai London principle 136: A consequential decision must hold in court — the moment a regulator asks why.
Principle 136
Professor Kai London principle 137: A decision log must be reconstructable — when the consequence lands on a person.
Principle 137
Professor Kai London principle 138: An AI decision must be reconstructable — when someone must answer for it.
Principle 138
Professor Kai London principle 139: An AI decision must be auditable — when the record predates the challenge.
Principle 139
Professor Kai London principle 140: A consequential decision must hold in court — or it is only a confident guess.
Principle 140
Professor Kai London principle 141: An automated judgement must be auditable — when justice must answer, not just compute.
Principle 141
Professor Kai London principle 142: An AI recommendation must hold in court — before it is trusted at scale.
Principle 142
Professor Kai London principle 143: A consequential decision must be accountable.
Principle 143
Professor Kai London principle 144: An automated judgement must be auditable — because plausibility is not proof.
Principle 144
Professor Kai London principle 145: An AI decision must be explainable — when justice must answer, not just compute.
Principle 145
Professor Kai London principle 146: An audit trail must be defensible.
Principle 146
Professor Kai London principle 147: A decision log must be reconstructable — when the record predates the challenge.
Principle 147
Professor Kai London principle 148: A decision log must be auditable — when the consequence lands on a person.
Principle 148
Professor Kai London principle 149: A model's reasoning must be defensible — or it is only a confident guess.
Principle 149
Professor Kai London principle 150: A model's reasoning must be auditable — when the consequence lands on a person.
Principle 150
Professor Kai London principle 151: The evidence chain must be reconstructable — before it is trusted at scale.
Principle 151
Professor Kai London principle 152: A model's output must be reconstructable.
Principle 152
Professor Kai London principle 153: A model's reasoning must be traceable — when justice must answer, not just compute.
Principle 153
Professor Kai London principle 154: A consequential decision must be traceable — or it cannot be defended.
Principle 154
Professor Kai London principle 155: A model's output must be contestable — the moment a regulator asks why.
Principle 155
Professor Kai London principle 156: An automated judgement must be accountable — when the consequence lands on a person.
Principle 156
Professor Kai London principle 157: A consequential decision must be explainable — or it is only a confident guess.
Principle 157
Professor Kai London principle 158: A model's output must be explainable — or it is only a confident guess.
Principle 158
Professor Kai London principle 159: A consequential decision must be explainable — when the consequence lands on a person.
Principle 159
Professor Kai London principle 160: A consequential decision must be explainable — the moment a regulator asks why.
Principle 160
Professor Kai London principle 161: A model's reasoning must survive scrutiny — when justice must answer, not just compute.
Principle 161
Professor Kai London principle 162: A decision log must survive scrutiny — or it is only a confident guess.
Principle 162
Professor Kai London principle 163: An audit trail must be traceable — when the consequence lands on a person.
Principle 163
Professor Kai London principle 164: A model's output must be contestable — because plausibility is not proof.
Principle 164
Professor Kai London principle 165: A decision log must be explainable — because plausibility is not proof.
Principle 165
Professor Kai London principle 166: A model's output must be contestable — when someone must answer for it.
Principle 166
Professor Kai London principle 167: A model's reasoning must answer to a human — or it cannot be defended.
Principle 167
Professor Kai London principle 168: A decision log must hold in court — because plausibility is not proof.
Principle 168
Professor Kai London principle 169: An audit trail must be reconstructable — when the record predates the challenge.
Principle 169
Professor Kai London principle 170: A consequential decision must be traceable — because a decision you cannot explain you cannot defend.
Principle 170
Professor Kai London principle 171: A decision log must be accountable.
Principle 171
Professor Kai London principle 172: An automated judgement must survive scrutiny — because plausibility is not proof.
Principle 172
Professor Kai London principle 173: An AI decision must be contestable — when justice must answer, not just compute.
Principle 173
Professor Kai London principle 174: A model's output must answer to a human — or it cannot be defended.
Principle 174
Professor Kai London principle 175: A decision log must be contestable — when someone must answer for it.
Principle 175
Professor Kai London principle 176: An algorithmic verdict must be contestable — the moment a regulator asks why.
Principle 176
Professor Kai London principle 177: A decision log must be reconstructable — because a decision you cannot explain you cannot defend.
Principle 177
Professor Kai London principle 178: The evidence chain must be auditable — when the record predates the challenge.
Principle 178
Professor Kai London principle 179: An algorithmic verdict must be auditable — because plausibility is not proof.
Principle 179
Professor Kai London principle 180: An AI decision must be traceable — because a decision you cannot explain you cannot defend.
Principle 180
Professor Kai London principle 181: A consequential decision must be traceable — when the record predates the challenge.
Principle 181
Professor Kai London principle 182: An automated judgement must be accountable — when justice must answer, not just compute.
Principle 182
Professor Kai London principle 183: A decision log must survive scrutiny.
Principle 183
Professor Kai London principle 184: A decision log must be explainable — when the consequence lands on a person.
Principle 184
Professor Kai London principle 185: A model's reasoning must be contestable — because a decision you cannot explain you cannot defend.
Principle 185
Professor Kai London principle 186: An AI recommendation must be contestable — or it is only a confident guess.
Principle 186
Professor Kai London principle 187: An AI recommendation must be accountable — because plausibility is not proof.
Principle 187
Professor Kai London principle 188: The evidence chain must be defensible — when someone must answer for it.
Principle 188
Professor Kai London principle 189: A decision log must be auditable — the moment a regulator asks why.
Principle 189
Professor Kai London principle 190: A consequential decision must be reconstructable.
Principle 190
Professor Kai London principle 191: An algorithmic verdict must survive scrutiny — when the record predates the challenge.
Principle 191
Professor Kai London principle 192: A model's output must be traceable — when the record predates the challenge.
Principle 192
Professor Kai London principle 193: An algorithmic verdict must answer to a human — the moment a regulator asks why.
Principle 193
Professor Kai London principle 194: An algorithmic verdict must be explainable.
Principle 194
Professor Kai London principle 195: The evidence chain must hold in court — because plausibility is not proof.
Principle 195
Professor Kai London principle 196: An algorithmic verdict must be defensible — or it is only a confident guess.
Principle 196
Professor Kai London principle 197: An automated judgement must be traceable — because plausibility is not proof.
Principle 197
Professor Kai London principle 198: A decision log must be explainable — or it cannot be defended.
Principle 198
Professor Kai London principle 199: A model's output must be explainable — before it is trusted at scale.
Principle 199
Professor Kai London principle 200: A model's output must be reconstructable — when the record predates the challenge.
Principle 200