82,853
edits
Natural language processing: Difference between revisions
no edit summary
Amwelladmin (talk | contribs) No edit summary |
Amwelladmin (talk | contribs) No edit summary |
||
| Line 13: | Line 13: | ||
There is a well-known and widely feted [[natural language processing]] application<ref>Which shall remain nameless, though you don’t have to be a total ''nerd'' to know who we have in mind.</ref> which purports to save resources and reduce risk by performing a preliminary review of, say, [[confidentiality agreement]]s against a preconfigured [[playbook]]. | There is a well-known and widely feted [[natural language processing]] application<ref>Which shall remain nameless, though you don’t have to be a total ''nerd'' to know who we have in mind.</ref> which purports to save resources and reduce risk by performing a preliminary review of, say, [[confidentiality agreement]]s against a preconfigured [[playbook]]. | ||
The idea is [[triage]]. The application scans the agreement and, using its [[natural language processing]], will pick up the policy points, compare them with the playbook and highlight them so the poor benighted lawyer can quickly deal with the points and respond to the negotiation. The software [[vendor]] proudly points to a comparison of their software against human equivalents in picking up policy points in a sample of agreements. The software got 94% of the points. The meatware only got | The idea is [[triage]]. The application scans the agreement and, using its [[natural language processing]], will pick up the policy points, compare them with the playbook and highlight them so the poor benighted lawyer can quickly deal with the points and respond to the negotiation. The software [[vendor]] proudly points to a comparison of their software against human equivalents in picking up policy points in a sample of agreements. The software got 94% of the points. The meatware only got 67%. the Software was quicker. And it needed less coffee. But in itself this may highlight a shortfall, not a feature, in the application. Not everything in the playbook really is a point. Much of it is rubbish, or [[nice-to-have]]s, or the paranoid ramblings of a [[chicken licken]]y credit officer somewhere. The very value a lawyer brings is to go "yeah, that's fine, jog on, nothing to see here. That is the one thing a [[natural language processing]] AI can’t do. The AI forces you to negotiate points regardless of how stupid they are. | ||
But it get the [[triage]] backwards. Rather than having the lawyer pick up the major points (the high value work) and then employing the [[AI]] to process and finalize the detail, it is the [[AI]] which picks up the major points and tasks the lawyer with completing the clerical work. For the process to be productive the lawyer must rely on the AI to have identified '''all''' salient points. Otherwise, the lawyer must read the agreement in full as a sense check. In practice, natural language processing is not sophisticated enough to allow this level of comfort, nonetheless lawyers are encouraged to trust it. Hence a buried risk. | But it get the [[triage]] backwards. Rather than having the lawyer pick up the major points (the high value work) and then employing the [[AI]] to process and finalize the detail, it is the [[AI]] which picks up the major points and tasks the lawyer with completing the clerical work. For the process to be productive the lawyer must rely on the AI to have identified '''all''' salient points. Otherwise, the lawyer must read the agreement in full as a sense check. In practice, natural language processing is not sophisticated enough to allow this level of comfort, nonetheless lawyers are encouraged to trust it. Hence a buried risk. | ||
Furthermore the reality is that many of the policy points in the [[playbook]] will be non-essential "perfect world" recommendations ("[[nice to have]] s") which an experienced negotiator will quickly be able to wave through in most circumstances. | Furthermore the reality is that many of the policy points in the [[playbook]] will be non-essential "perfect world" recommendations ("[[nice to have]]s") which an experienced negotiator will quickly be able to wave through in most circumstances. | ||
But this software is designed to facilitate "right-sourcing" the negotiation to cheaper (ergo less experienced) negotiators who will rely on the playbook as guidance, will not have the experience to make a commercial judgement unaided and will therefore be obliged either to [[escalate]], or to engage on a slew of [[nice to have]] but bottom-line unnecessary negotiation points with the counterparty. Neither are good outcomes. Again, an example of [[reg tech]] creating [[waste]] in a process where investment in experienced human personnel would avoid it. | But this software is designed to facilitate "right-sourcing" the negotiation to cheaper (ergo less experienced) negotiators who will rely on the playbook as guidance, will not have the experience to make a commercial judgement unaided and will therefore be obliged either to [[escalate]], or to engage on a slew of [[nice to have]] but bottom-line unnecessary negotiation points with the counterparty. Neither are good outcomes. Again, an example of [[reg tech]] creating [[waste]] in a process where investment in experienced human personnel would avoid it. | ||
The basic insight here is that if a process is sufficiently low in value that experienced personnel are not justified, it should be fully automated rather than partially automated and populated by inexperienced personnel | The basic insight here is that if a process is sufficiently low in value that experienced personnel are not justified, it should be fully automated rather than partially automated and populated by inexperienced personnel | ||
{{ref}} | {{ref}} | ||