Chapter 33
MY EYES WERE on Marcus Mason when the judge told me to call my next witness.
When I said the name Naomi Kitchens, he leaned back in his chair as though he were dodging a roundhouse punch at his chin.
He was clearly surprised, but I realized I couldn’t tell if that was because he wasn’t expecting the former ethicist to be my next witness or because he wasn’t expecting her to testify at all.
The latter would have confirmed that he was aware of and had sanctioned the intimidation tactic initiated against her the night before.
But when he was finished dodging the invisible punch, his hands immediately went to the stack of files on the defense table, and he went three deep to pull an inch-thick file I assumed contained his prep material on Kitchens.
There was no thicker file in the stack, and the fact that it wasn’t on top told me that Marcus had simply not been expecting Kitchens at this point in the trial.
That further suggested that he hadn’t been aware of the events of the night before.
This was good, because the move at the hotel had been a critical misstep by whoever was responsible.
It had failed to stop Kitchens from testifying, and it was going
to help me avoid being waylaid by whatever was in that fat file Marcus had just pulled out.
After Naomi was sworn in by the court clerk and asked to spell her name for the record, she took the stand and immediately glanced out at the gallery, where her daughter sat next to Cisco.
She nodded slightly and steeled herself for what was coming next.
At the Redbird we had gone over how it would go from my side of the lectern.
It was the defense side that was the unknown.
I had told her to find Lily in the gallery and use her as a focal point when things got stressful on the stand.
“Good afternoon, Professor Kitchens,” I began. “Is that your real name, Naomi Kitchens?”
“It’s my legal name now,” Kitchens responded.
“You had it changed?”
“A long time ago, yes.”
“What was your given name and what made you change it?”
“My birth name was Alison Sterling. I changed it twenty years ago to protect myself and the child I was carrying.”
I saw her eyes go out to her daughter as she answered.
“Protect the child from whom?” I asked.
“My ex-boyfriend,” she said. “This was back in Pennsylvania, where I grew up.”
“Can you tell the jury why you felt the need to take these steps?”
“Well, he was a bad man. He was committing crimes and I realized I had to get away from him. So I left. I went to California and legally changed my name so he wouldn’t be able to find us.”
“Who is us?”
“My daughter and I.”
“How old is your daughter now?”
“She’s nineteen.”
“And was the man you ran from her father?”
“Yes.”
“Did he ever find you after you escaped?”
“No, he went to jail for many years. Prison, actually.”
“Do you know what crime he was convicted of?”
“Robbery and assault. He shot a man but the man didn’t die.”
“Were you involved in any way with these crimes?”
“No, but… we lived on the money he stole. I knew that. It was one of the reasons I needed to get away from him.”
“Were there other reasons?”
“He was violent. I was afraid he would hurt the baby.”
“What was this man’s name?”
“Quentin Holgard.”
“So if Quentin Holgard came into this courtroom and said you committed these crimes with him, would he be telling the truth?”
“No, he would be lying.”
My last question was a guess. But I had to get in front of any move the Masons might make.
They might have Quentin Holgard teed up and ready to go as a rebuttal witness, thereby keeping his name hidden and off the approved-witness list. I didn’t know what the defense plan was but I wanted to be ready for anything.
Feeling that I had put what I could on the record, I dropped into my original plan for Kitchens’s testimony.
“Okay, so you came out to California to escape from this man, and then what happened?” I asked.
“I worked and I went to school up in the Bay Area,” Kitchens said.
“What school?”
“My first degree was from USF and—”
“USF?”
“Sorry, University of San Francisco. I then got a master’s at UC Berkeley and later a doctorate from Stanford.”
I walked her quickly through her degrees in order—computer science, psychology, and finally sociology.
“I guess I should be calling you Dr. Kitchens,” I said.
“I prefer just Naomi,” she said.
“Okay, Naomi. And did you pay your way through all these schools?”
“Yes. I worked and I got some scholarship money, a few research grants. But I also had student loans that I’m still paying off.”
This brought a low murmur of laughter in the courtroom.
“You are apparently not alone in that,” I said. “When you worked, what was the job or jobs you took?”
“I was a coder for various companies,” Kitchens said. “I worked for Microsoft, Apple, a few others.”
“What’s a coder do?”
“Writes operating code for various apps.”
“Okay. And you did all of this while being a single mother and going to school?”
“Yes.”
“What was your career goal with all these degrees?”
“I wanted to be a teacher at the college level. I wanted to be a professor.”
“And did you accomplish that?”
“Yes. My first job was at USF, and after I got my doctorate I stayed at Stanford for the next three years.”
“What happened that made you leave Stanford?”
“I got a job offer from Tidalwaiv that would almost double my income. I took it so I could provide a better life for my daughter.”
“Can you tell us what that job entailed?”
“I was an ethicist primarily assigned to Project Clair.”
I smiled and raised my hands from the lectern in a What gives? gesture.
“I have to say, I’m not sure what an ethicist is or does,” I said. “Can you explain it to us?”
“Clair was a generative artificial-intelligence project,” Kitchens said.
“At the time, it was the new frontier of AI technology. There weren’t many rules and there was almost zero government oversight.
It was very competitive, and the tech companies started hiring people to make sure these programs and apps were created in a responsible way.
Generative AI was going to change the world—it already has.
The ethicist was sort of the human conscience of the project.
I was supposed to help make sure there were guardrails in place to protect the people these systems would serve. ”
“‘Supposed to’?”
“In some cases, although the company wants to say it’s ethical, it doesn’t work out that way. The stakes involved are extremely—”
Marcus Mason stood and objected.
“Your Honor, by talking in generalities, the witness is insinuating that unethical behavior occurred at Tidalwaiv on Project Clair,” he said.
“There has been absolutely no evidence of that presented at trial, because it doesn’t exist. I ask that the question and answer be stricken and the jury be so instructed. ”
Judge Ruhlin looked at me for a response.
“Judge, first of all, I would ask the court to instruct counsel not to incorporate his closing argument into his objection. Second, I am laying the groundwork so that the jury understands what this witness’s job was at Tidalwaiv and, more specifically, on Project Clair.”
“I’m going to sustain the objection,” Ruhlin said. “Mr. Haller, let’s move on to testimony directly related to the cause of action.”
“Yes, Your Honor,” I said. “A moment, please.”
I looked down at my legal pad and flipped to the next page, skipping several questions that I now knew would not get past the defense’s objections.
“Okay, Naomi, let’s talk about Project Clair,” I said. “When were you assigned to it?”
“I was hired by Tidalwaiv in late 2021,” Kitchens said. “After some training I was assigned to Project Clair in January of ’22.”
“Was that the starting point of the project?”
“No, the project was well down the road. I reviewed code and company directives that were three years old when I was getting up to speed on it.”
“So they brought the ethicist in late to the project.”
Marcus jumped up with an objection, arguing that my statement assumed facts not in evidence. The judge sustained the objection without asking me to respond. I knew the objection was valid. I just wanted the jury to put the question in a back pocket for later. I moved on.
“Dr. Kitchens, you—”
“Naomi.”
“Right, Naomi. Earlier you called Project Clair a generative AI program. Can you tell the jury what generative AI means?”
“Of course. Gen AI simply means that these models, like the Clair app, for example, generate new data, whether it be video images or text, from the underlying data they were trained with.”
I liked how she turned to look at the jury as she spoke. I had said to her at lunch, “You’re a teacher. Be a teacher on the witness stand.” She was doing it now, and I believed it was being received well by her pupils, the jurors.
“So, then, would it be fair to say that it is not simply data in, data out?” I asked.
“Correct,” Naomi said. “That is the generative part of the equation. The training is ongoing. These large language models are constantly bringing data in and from that learning more.”
“‘Large language model’? Can you explain that?”
“It’s a machine-learning model designed for natural language generation.
It’s trained on vast amounts of data and text, and then analyzes and sifts it all for patterns and relationships when prompted to have a conversation or answer a question.
These models acquire predictive power in terms of human language.
The ongoing downside, however, is they also acquire any biases or inaccuracies contained in the training data. ”
“You’re saying ‘garbage in, garbage out.’”
“Exactly. And that’s where the ethicist comes in. To make sure there are guardrails that keep the garbage from ever getting in.”
I paused for a moment as I made a shift back toward my case.